Automation doi: 10.3390/automation7050139

Authors: Francesco Pierri Graziano Carriero Monica Sileo Fabrizio Caccavale

This paper addresses the design of an adaptive observer for a class of nonlinear cascade systems with partially measured states and unknown constant parameters. The considered systems have a cascade structure involving an unmeasured-state subsystem and a measured-state subsystem. The unknown parameter vector enters the measured state dynamics through a known distribution matrix, while the unmeasured state dynamics are described by a lower triangular subsystem satisfying suitable conditions. An adaptive observer is proposed to reconstruct the unmeasured state variables and to compensate for the effect of the unknown parameters using only the measured output and the known input. Sufficient gain conditions are derived through a Lyapunov-based analysis to guarantee asymptotic convergence of the state estimation errors in the absence of uncertainties, while ensuring boundedness of the parameter estimation error. The effect of bounded model uncertainties is then analyzed, and a σ-modified adaptive law is introduced to prevent parameter drift and guarantee uniform ultimate boundedness of the estimation errors. The proposed framework is illustrated through a reduced-order marine vessel model involving slowly varying environmental bias states and an unknown constant disturbance term. Numerical simulations illustrate the convergence properties and the robustness of the observer under bounded model uncertainties. Additional numerical tests with noisy output measurements are also reported to assess sensitivity to measurement noise.

Automation doi: 10.3390/automation7050138

Authors: Bo Nørregaard Jørgensen Zheng Grace Ma

The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation.

Automation doi: 10.3390/automation7050137

Authors: Samara H. Al-dahlaki Safanah M. Raafat Shibly A. AI-Samarraie Amjad J. Humaidi

This paper proposes a hybrid integral sliding mode and finite-time control (ISMC–FTC) strategy for a two-degree-of-freedom (2DOF) robotic manipulator, targeting finite-time stability and high-precision elliptical trajectory tracking. To ensure practical deployment, the controller explicitly enforces actuator saturation limits, joint kinematic bounds, workspace constraints, and prescribed stabilization time requirements. Particle swarm optimization (PSO) is employed to tune the FTC parameters, minimizing convergence time while guaranteeing constraint satisfaction. By integrating ISMC’s inherent robustness against matched disturbances with a PSO-optimized FTC, the scheme eliminates the reaching phase and ensures rapid, finite-time convergence. The simulation results demonstrate that the proposed approach significantly reduces stabilization time for both joints, maintains exceptionally low tracking errors, and enforces all physical constraints under bounded disturbances and model uncertainties through explicit saturation limits and sliding manifold invariance. These results validate the framework’s effectiveness and highlight its potential for safety-critical, high-precision robotic applications requiring guaranteed finite-time performance. Robustness is further validated under simultaneous disturbances and uncertainties, where the controller maintains stable performance and consistently satisfies the required robust stability condition.

Automation doi: 10.3390/automation7050136

Authors: Yoisdel Castillo Alvarez Anibal Salvador Wenceslao Ferro Diaz Deyvi Gair Baltazar Cari Reinier Jiménez Borges Carlos Diego Patiño Vidal Fanny Mabel Carhuancho León Romel Ángel Cárdenas Javier Roberto Pfuyo Muñoz

The automation of biodigesters using programmable logic controllers (PLCs) is well established, yet mixing schedules are configured empirically, regardless of substrate characteristics. This study proposes a Substrate-Driven PLC Control (SD-PLC) methodology for fixed-dome biodigesters, in which every timing setpoint is derived from a measured substrate property: the homogenization pulse from the rheological mixing time (Metzner–Otto regime), the maintenance interval from a stratification and a substrate–biomass contact criterion, the duty-cycle ceiling from a net energy balance, and a safety gate from acid–base behavior (pH/EC). The methodology is instantiated on a 13.86 m3 fixed-dome biodigester in the Chillón Valley (Lima, Peru), co-digesting organic waste and guinea pig manure (30:70; theoretical methane potential 371 mL CH4 g−1 VS; field conversion ≈ 21%). The impeller operates in the transitional regime (Re≈0.9–2.6×103), the homogenization pulse is ≈22 min, and the reconciled duty cycle (δ≈0.13) remains a factor of four below the energy ceiling (δ*≈0.56). An influence × feasibility matrix identifies a low-cost sensor set (pH, electrical conductivity, temperature, level) that gates mixing. A reduced COD-based dynamic model shows that, under acid shock, continuous mixing suppresses methanogenesis through shear while the absence of mixing fails through contact deficit, so that only the stability-first strategy preserves the process. The principal contribution is the methodology itself—a reproducible mapping from substrate characterization to PLC timing design—rather than the hardware.

Automation doi: 10.3390/automation7050135

Authors: Wenchao Yang Shihao Liu Junlin Zeng Rongchang Li Xiaoyu Wen Yuyan Zhang Yuanji Liang

As an important part in the mining crushing and grinding circuit, the high-pressure grinding roll (HPGR) is essential for energy efficiency, cost reduction, quality improvement and efficiency gains; therefore, it plays a key role in sustaining stable production and intelligent control of mining operations. Its instantaneous throughput and processing capacity directly influence the efficiency of the comminution system, the compatibility of production scheduling, and overall energy consumption. Therefore, these metrics serve as important indicators for intelligent optimization and stable operation. However, it is a difficult task to predict the throughput of HPGRs by traditional purely data-driven models. The process is characterized by strong nonlinearity, significant time-lag effects, and limited physical consistency. To address these challenges, this work develops a tailored throughput prediction framework for HPGRs by combining Gated Recurrent Units with Physics-Informed Neural Networks (GRU-PINN), which integrates an HPGR-specific volumetric throughput mechanism as a dedicated physical constraint. This approach first exploits the GRU network’s “reset” and “update” gates to selectively filter historical operating information while adaptively updating the current process features. This allows the model to better capture complex long-term temporal dependencies in the production data. Additionally, based on volumetric analysis and the principles of bed comminution, the HPGR throughput formula is incorporated into the loss function as a physical constraint. Together, these components establish a joint optimization framework that combines data-driven learning with physical constraints to correct prediction biases generated by the neural network during abrupt changes in operating conditions. The proposed GRU-PINN model was validated using real operational data collected from an industrial mining site. The results show that the proposed framework significantly improves the physical consistency of HPGR throughput predictions and, at the same time, effectively corrects prediction biases, which are commonly observed in conventional purely data-driven models under complex operating conditions. It also exhibits improved robustness and lower inference latency. Compared with other benchmark models, the proposed method displays superior overall performance across key evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), prediction accuracy, and inference speed. These findings confirm that the proposed method greatly enhances the physical interpretability of the prediction model without compromising accuracy. Consequently, it lays a solid foundation for process parameter optimization and intelligent control of HPGR system.

Automation doi: 10.3390/automation7050134

Authors: Majid Ghorbani Omar Hanif Patrick Gruber Aldo Sorniotti Komeil Nosrati Aleksei Tepljakov Eduard Petlenkov Umberto Montanaro

Stabilisation of autonomous vehicle platoons becomes challenging in the presence of uncertain delays in actuator dynamics and vehicle-to-vehicle communication. This paper presents a distributed fractional-order proportional derivative (FOPD) control protocol for a platoon operating under various communication topologies, with bounded uncertainties in actuator lag and communication delay, respectively. It builds on an auxiliary function-based robust stability approach that provides a geometric interpretation of the delay-uncertain characteristic family and eliminates the need for exhaustive parameter gridding. This is followed by a numerical design procedure that evaluates the derived robust stability conditions on explicitly stated frequency grids. Numerical analysis of heterogeneous platoons indicates that the FOPD strategy produces bounded stable responses for the sampled uncertainty realisations and exhibits improved tracking behaviour compared with the integer-order PD baseline using the same numerical values of KP and KD. It also provides better damping of oscillations and reduced tracking errors under uncertain operating conditions.

Automation doi: 10.3390/automation7050133

Authors: Kan Xu Yingkai Xia

To complete scientific experiments on an underwater tension leg platform, a new cable-driven underwater vehicle is proposed, which is subjected to not only unknown underwater disturbances but also time-varying nonlinear cable tractions. To achieve displacement tracking control despite the high-order nonlinearities and matched and mismatched uncertainties with unknown upper bounds, a radial basis function neural network-based adaptive multiple-surface sliding control strategy (RBFNN-AMSC) is proposed. Utilizing a backstepping design procedure and the Lyapunov approach, the system is decomposed into six subsystems, and the stability is ensured. By employing multiple-surface sliding mode control and exponential reaching law design, the robustness and convergence rate of each subsystem are improved. Moreover, with an adaptive radial basis function neural network, the influences of matched and mismatched uncertainties are compensated, which improves the system anti-jamming capability and avoids the “differential explosion” problem. To verify the effectiveness of the proposed approach, numerical simulations are carried out under different conditions, which show that the proposed control strategy can achieve accurate displacement tracking control regardless of complex nonlinear dynamics and various unknown external disturbances.

Automation doi: 10.3390/automation7040132

Authors: Farid Abitaev Bagdat Azamatov Suresh Alapati Vyacheslav Kornev Rustam Zhanbosinov Karlygash Alibekkyzy Madina Bazarova

Digital transformation in agricultural production requires automated approaches for monitoring, risk assessment, and decision support under uncertainty. This study proposes an automated risk assessment and control framework for feed production in digital agroengineering systems. The framework is designed for the quantitative evaluation of producer and consumer risks arising during the control of key feed-quality parameters, using the case of feed production for cattle in the OHMK agricultural holding. The proposed approach integrates probabilistic modeling, simulation-based risk estimation, fuzzy logic, expert evaluation, and a multi-agent representation of agroengineering processes. A three-dimensional risk model is developed to represent producer risk, consumer risk, and actuarial risk as interconnected components of a digital control environment. In addition, a fuzzy model is introduced to assess the robustness and digital maturity of management functions, including organization, planning, motivation, and control. Computer experiments based on statistical data for crude protein content in silage demonstrate that control risks depend nonlinearly on measurement uncertainty, parameter variability, and normative thresholds. In the analyzed single-indicator case study, the arithmetic mean of crude protein content in silage was 7.5% of dry matter, the standard deviation was 0.5, and the Weibull approximation parameters were α = 1.0, β = 2.5, and γ = 6.0. Under the most sensitive normative threshold scenario, producer risk increased to approximately 25%, while consumer risk showed a lower but nonlinear increase with measurement uncertainty. The results show that producer risk may reach significant levels when measurement uncertainty becomes comparable with the variability of the controlled parameter. The proposed framework can serve as a computational basis for future automated monitoring, risk-aware control, and decision-support systems in Industry 4.0-oriented agricultural production.

Automation doi: 10.3390/automation7040131

Authors: Cristian Zambrano-Vega Washington Chiriboga-Casanova Byron Oviedo Efraín Díaz-Macías Edgar Suárez Bardelline

Automated surface inspection of balsa wood panels is challenging because defects may be small, elongated, weakly contrasted, or visually similar to natural grain patterns. This study proposes YOLOv11–BiFPN–DAAF, an enhanced object-detection architecture that combines bidirectional multi-scale feature fusion with adaptive dual-attention feature refinement. The task was formulated as single-class detection, with all anomalous surface regions labeled as Defect. The dataset comprised 508 manually annotated RGB images, including independent internal and external production test sets. A preliminary screening identified YOLOv11-m512 as the reference configuration, followed by a controlled 2×2 factorial ablation comprising the baseline, BiFPN, DAAF, and their combined integration. Each configuration was trained using five independent random seeds under identical experimental conditions. On the validation set, the combined architecture achieved a precision of 0.893±0.004, recall of 0.848±0.006, mAP@0.5 of 0.897±0.004, and mAP@0.5:0.95 of 0.389±0.004. Relative to the baseline, the largest improvement was obtained for mAP@0.5:0.95, with a relative gain of 9.89%, indicating improved localization under stricter IoU thresholds. The improvement was retained on the independent internal test set, where the proposed architecture reached mAP@0.5 and mAP@0.5:0.95 values of 0.892±0.005 and 0.384±0.006, respectively. On the external production test set, the corresponding values were 0.865±0.007 and 0.358±0.008, representing absolute improvements of 0.034 and 0.042 over the original YOLOv11 baseline. Under the matched experimental protocol, YOLOv11–BiFPN–DAAF also achieved the highest principal detection metrics among the evaluated representative detectors. Although BiFPN and DAAF introduced a moderate computational overhead, the architecture maintained an inference time of 9.6±0.3 ms per image. These findings support the potential of the proposed architecture for automated balsa wood panel inspection, while broader multi-site and hardware-level validation remains necessary before large-scale industrial deployment.

Automation doi: 10.3390/automation7040130

Authors: Dezheng Ma Lan Tang

Accurate multi-object tracking and metric localization support traffic monitoring and cooperative intelligent transportation at complex intersections. This study presents a fixed-camera vision-map fusion framework that addresses two practical difficulties: axis-aligned boxes poorly represent turning vehicles, and unconstrained image-plane tracking can produce physically implausible trajectories. A map-aided frontend first generates candidate detections using improved You Only Look Once version 8 nano (YOLOv8n) horizontal bounding box (HBB) branch and an improved YOLOv8 oriented bounding box (OBB) branch. A high-definition (HD) map selector then retains the candidate geometry consistent with the straight-driving or turning region and converts it into a unified detection record. The selected reference point is projected to the ground plane through an offline-estimated homography, whereas the appearance feature bypasses the homography and is passed directly to the association stage. The tracking backend uses a 12-dimensional joint image/metric state, symmetric central-difference evaluations of the process and measurement functions, appearance-motion association, and a feasible-road projection derived from HD-map lane polygons. On the evaluated public sequences, the complete configuration achieved a multiple object tracking accuracy (MOTA) of 74.5%, an identification F1 score (IDF1) of 82.6%, 614 identity switches, and a throughput of 26.8 frames per second (FPS) on an RTX 4090 workstation. In a descriptive Vehicle-in-the-Loop case study involving one instrumented vehicle at one intersection, the overall localization mean absolute error (MAE) was 0.180 m, compared with 0.208 m for the baseline end-to-end configuration. These results indicate the feasibility of combining branch-specific vehicle geometry with map-constrained tracking; controlled same-detector comparisons, repeated multi-vehicle trials, and embedded-device latency and power profiling remain necessary for broader claims.

Automation doi: 10.3390/automation7040129

Authors: Rafael Reveles-Martínez Hamurabi Gamboa-Rosales Huizilopoztli Luna-García Erika Sánchez-Femat Javier Saldívar-Pérez Flabio D. Mirelez-Delgado Umanel A. Hernández-González Carlos E. Galván-Tejada Jorge I. Galván-Tejada José M. Celaya-Padilla

Lane keeping under degraded visual confidence remains challenging because most existing approaches focus either on improving lane-feature extraction or on estimating vehicle motion, while giving less attention to how unreliable visual measurements should modify the estimator–controller interaction in a physical closed-loop system. This paper presents a confidence-gated Kalman lane-keeping architecture for a 1:10-scale autonomous vehicle. The methodological contribution lies in the direct coupling of visual confidence, state estimation, and steering control: unreliable lane measurements are down-weighted through confidence-dependent measurement noise, while the propagated lane-relative state remains available to the controller. The primary experimental unit is the run, defined as one logged lap under one control configuration; frame-level summaries are retained only as historical reproducibility material. In the run-level comparison, the autonomous vision, inertial measurement unit (IMU)-feedforward, and Kalman filter (KF) group KF_G1—the first inferential KF generation—had lower mean absolute error than the human baseline, while vision and KF_G1 had overlapping run-level confidence intervals for absolute error. KF_G1 shifted the mean signed bias closer to the lane reference than the vision and IMU groups, but with higher run-level spread than vision. KF_G2, the second observational KF generation, is reported only as an observational generation comparison, so no causal claim is made for its estimator–controller correction weight Ks=0.15 setting. KF_G3, the single descriptive adverse-condition KF run, is reported without an estimable confidence interval or population-level adverse-illumination inference. A further limitation is that the inertial prediction pathway was inactive in the logged Kalman-filter runs because the inertial coupling coefficient α=0. The main contribution is a reproducible run-level evaluation of confidence-gated estimator–controller coupling that distinguishes supported evidence from observational and descriptive cases.

Automation doi: 10.3390/automation7040128

Authors: Raya Abu Shaker Yousef Sardahi Ahmad Alshorman

Type I diabetes mellitus (T1DM) is a chronic metabolic disease resulting from insufficient insulin secretion into the bloodstream‚ causing elevated blood glucose concentrations to dangerous levels․ Automated regulation of blood glucose levels in T1DM can be modeled as a nonlinear‚ uncertain‚ and disturbance-affected closed-loop control process with a time delay‚ time-varying insulin sensitivity, and imperfect glucose measurements. This paper presents the design, multi-objective tuning, and robustness evaluation of a fuzzy logic controller (FLC) for automated insulin-infusion regulation. The proposed FLC uses the glucose tracking error and its time derivative as feedback signals to determine the required insulin control action and maintain glucose within the desired range of (70–160 mg/dL). The controller parameters are optimized using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to address three competing control objectives: minimizing hypoglycemia risk, minimizing hyperglycemia risk, and reducing total insulin usage. The resulting Pareto-optimal solutions provide a set of trade-off controller designs for decision-makers based on safety, performance, and insulin-efficiency requirements. The robustness of the proposed automated control framework is evaluated under challenging operating conditions, including elevated initial glucose levels, model-parameter uncertainties, external disturbances, variations in insulin sensitivity, distorted glucose measurements, and delayed insulin infusion. A comparative study with a linear quadratic regulator-based controller (LQRC) is conducted as a benchmark. Simulation results demonstrate that the optimized FLC provides superior closed-loop performance and stronger robustness than the LQRC across all tested scenarios. The proposed fuzzy-control framework, therefore, offers a promising automation-based strategy for resilient glucose regulation under uncertainty, measurement imperfections, and actuation delays.

Automation doi: 10.3390/automation7040127

Authors: Konstantinos Zervakis Ilias Panagiotopoulos

Wildfires generate rapidly evolving hazard landscapes that disrupt ground-based logistics and render conventional disaster-response operations ineffective, motivating the use of unmanned aerial vehicles (UAVs) in civil-protection missions such as medical resupply, casualty search-and-rescue, and perimeter surveillance. Existing evaluations, however, share a common limitation: they assess performance using navigation-centric metrics—primarily success rate—without accounting for the temporal value of the mission objective. This paper characterises, within each planner family, how a single risk coefficient ρ governs the trade-off between navigation success and time-decaying mission value: holding each family’s algorithm and replan trigger fixed and sweeping only ρ isolates its effect, so that the risk setting maximising a family’s navigation success need not maximise its mission value. To study this, FLARE is introduced, a deterministic benchmark for fire-landscape adaptive risk evaluation, inspired by recent Greek wildfire events; all hazard dynamics (fire spread, structural collapse, moving obstacles, dynamic no-fly zones) are modelled as benchmark abstractions rather than incident-specific reconstructions. FLARE evaluates eight planners spanning six risk-handling paradigm families, isolating the risk coefficient by sweeping ρ within each family (algorithm fixed) with A* as the risk-blind static reference, and quantifies mission impact—medication efficacy, casualty survival, and data freshness—through a strictly time-decreasing mission-score function. The mission-value leverage of ρ is strongly family-dependent: decisive for the incremental soft-cost-inflation family—whose success-optimal ρ collapses its mission value (0.91 → 0.41)—strong for the worst-case (CVaR) family, moderate for the sampling family, and negligible for the reactive, hard-threshold and frequent-replan families. In some families the success-optimal and mission-optimal ρ diverge—a within-family ranking inversion—while in others they coincide; because the algorithm is fixed across each sweep, the divergence is attributable to ρ alone. Success of navigation is therefore a necessary but insufficient proxy for mission effectiveness, and the risk coefficient is a meaningful per-family parameter for mission-aware configuration.

Automation doi: 10.3390/automation7040126

Authors: Chitranjanjit Kaur Sumit Chopra Chitta Tripathy

In Industry 4.0 manufacturing, predictive maintenance has emerged as a key focus to ensure the reliability of operations, minimize downtime, and contribute to equipment safety. This study proposes a predictive maintenance framework for additive manufacturing systems (AMSs) using an explainable artificial intelligence (XAI) and machine learning (ML) approach, which is supported by real-time multi-sensor monitoring and is synchronized with a digital twin architecture. A heterogeneous dataset of over 10,000 observations and 13 sensor attributes was gathered from a sensing architecture with ESP32 cameras designed to handle heterogeneous signals from thermal, environmental, mechanical and safety-related sensors. Domain-aware feature engineering was done to gain insights into operation indicators such as temperature instability, vibration degradation, smoke risk, humidity anomalies and aggregated maintenance risk scores. Multiple predictive models, such as the Random Forest model, XGBoost model, Logistic Regression model, K-Nearest Neighbours classifier, Multi-Layer Perceptron model, and ARIMA forecast model, were comparatively assessed under highly imbalanced maintenance conditions. The results showed that ensemble learning methods, especially XGBoost and MLP, had better recall and ROC-AUC for fault detection of maintenance-critical problems. SHAP and LIME analyses then showed that a number of physically meaningful indicators, including thermal instability, vibration anomalies, smoke-related features, and aggregated risk scores, have a significant impact on maintenance predictions and hence provide better operational interpretability and maintenance transparency.

Automation doi: 10.3390/automation7040125

Authors: Ata Jahangir Moshayedi Babar Hussain Shah Amir Sohail Khan Seyyed Ali Eftekhari Amin Kolahdooz David Bassir

Accurate monitoring of atmospheric ammonia (NH3) is important for air-quality assessment and sustainable agriculture, but available datasets differ in spatial resolution, temporal coverage, units, and physical meaning. This study presents a feature-based machine-learning framework with train–test leakage-controlled preprocessing to evaluate relative NH3 classification consistency across three datasets over China: CAMS GEI, CAMS EAC4, and MEIC. Statistical descriptors were extracted for four spatial–temporal cases: Province–Year, Zone–Year, Province–Season–Year, and Zone–Season–Year. Six classifiers were evaluated using chronological testing and cross-validation. CAMS GEI provided the broadest spatial–temporal coverage, while MEIC showed comparatively stable classifier behavior. The best-performing ensemble models commonly achieved accuracies between 0.97 and 0.99 in the main province-level cases. Mean Absolute Value (MAV) was the leading feature in several province-level analyses, with a maximum reported contribution of 64.26%. These scores describe the separability of threshold-derived reference classes and should not be interpreted as an independent physical prediction of NH3 from external atmospheric drivers.

Automation doi: 10.3390/automation7040124

Authors: Kamen Kamenov Viktor Angelov Lyubomir Karlov Krastyo Buchkov

Scalable Modular Instrumentation for Laboratory Experiments (SMILE) is a lightweight framework for the rapid development and networking of laboratory instrumentation demonstrated with a low-cost Arduino micro-controller. The approach extends the fast-prototyping paradigm of Arduino by enabling a seamless transition from standalone devices to distributed, network-accessible systems without requiring complex control infrastructures. The system architecture follows a simplistic and intuitive development workflow: devices are first implemented and defined through a human-readable serial interface, which is then reused without modification by a Python-based driver. The driver can be directly accessed or enabled as a network service via ZeroRPC, allowing transparent remote access to instrument’s functionality. A key feature of SMILE is the automatic mapping of serial commands to Python functions, which facilitates immediate integration of newly defined device commands into higher-level control and automation workflows. SMILE design allows heterogeneous system integration with both custom-built instruments and laboratory equipment with standard interfaces (e.g., GPIB, RS232/485, USB, Ethernet) to be incorporated into a unified distributed system through lightweight software layers. Presented demonstrator examples and test results show that SMILE provides a lightweight and accessible approach for physics laboratory automation, conceptually inspired by distributed control systems such as TANGO and EPICS, while remaining focused on small-scale experiments and rapid prototyping.

Automation doi: 10.3390/automation7040123

Authors: Yunong Zhang Jielong Chen Zhuosong Fu Guangyu Long

In this paper, the position–orientation tracking of the Stewart platform is investigated. We focus on the target-oriented tracking task, which usually occurs in the application of spotlights and cameras. Specifically, the mobile plate of the Stewart platform is always oriented to an immobile point while tracking a desired path. Based on the velocity-level kinematics and zeroing neural dynamics (ZND), a kinematic tracking model is proposed for the position–orientation tracking task. In addition, a robust ZND (RZND) model is further proposed against two kinds of disturbances. Theoretical analyses are presented to show the convergence properties of the proposed models. Discrete-time algorithms of the models are also developed for the convenient implementation. According to the comparative simulations, both the ZND and RZND algorithms accomplish the position–orientation tracking task without the disturbance, and the disturbance-suppression capability of the RZND algorithm is substantiated.

Automation doi: 10.3390/automation7040122

Authors: Alok Tiwari Yasser Qaffas

Artificial intelligence (AI) and the Internet of Things (IoT) are converging to create densely sensed, connected, and increasingly automated urban environments. However, research on AI–IoT integration remains dominated by optimisation-centric perspectives that under-specify spatial reasoning, physical actuation, and institutional accountability. This perspective addresses that gap by developing closed-loop urban automation as an analytical lens for understanding how AI–IoT systems move from urban sensing to consequential intervention. Drawing on a narrative, theory-driven synthesis of literature on Urban AI, IoT, Physical AI, spatial intelligence, digital twins, robotics, automation, and governance, the paper differentiates the framework from AIoT, cyber-physical systems, embodied AI, and optimisation-centric smart-city models. It identifies the distinctive conditions of urban automation, proposes six examinable propositions, and outlines implications for planning support, intelligent control, human oversight, and democratic legitimacy.

Automation doi: 10.3390/automation7040121

Authors: Yakup Kılıçaslan Sami Karadeniz

In aerospace manufacturing, validating heavy-duty automated production tooling and Ground Support Equipment (GSE) traditionally requires costly and time-consuming physical proof load testing. This study proposes a novel Virtual Certification framework that utilizes a high-fidelity Multiphysical Digital Twin driven by a Software-in-the-Loop (SiL) co-simulation architecture (integrating Siemens NX MCD, SIMIT, and TIA Portal) to retroactively diagnose mechanical failures and virtually validate design modifications prior to physical manufacturing. The dual-focus methodology is rigorously applied to a physical case study: an over-constrained (hyperstatic) 4-point aerospace lifting system designed for a 26.48 kN fuselage section that suffered a catastrophic mechanical stall during a 39.24 kN physical proof load verification. While conventional static dimensioning models erroneously predicted a nominal drive torque of only 4.56 Nm, the high-fidelity dynamic twin (incorporating a non-linear exponential Stribeck friction model) calculated the transient mechanical resistance causing the stall, capturing a peak load of 46.2 Nm at the motor shaft. The SiL co-simulation revealed that the rigid positional synchronization logic enforced by the PLC inadvertently amplified localized boundary friction, driving the actuators beyond their rated 6.4 Nm capacity. Based on this forensic diagnosis, a remedial powertrain featuring an 8.0 Nm stepper motor coupled with a 16:1 planetary gearbox was integrated and virtually certified. The framework confirmed that the upgraded architecture successfully attenuated the hyperstatic resistance, reflecting a peak load of only 3.0 Nm at the motor shaft and guaranteeing a stable Safety Factor of 2.66. By bridging the reality gap without iterative physical prototyping, this framework establishes a scalable, “First-Time-Right” validation paradigm for multi-point automated manufacturing mechanisms.

Automation doi: 10.3390/automation7040120

Authors: Abhishek Pandey Pramod Kumar Sahoo Tapan Kumar Khura Dilip Kumar Kushwaha Roaf Ahmad Parray Jeetendra Kumar Ranjan Md. Ashraful Haque Susheel Kumar Sarkar Rohit Gaddamwar Nrusingh Charan Pradhan

Brinjal (Solanum melongena L.) is an important vegetable crop worldwide, but its cultivation faces challenges from pests, diseases, and variable environmental conditions that negatively affect quality and yield. Accurate fruit detection in natural field conditions is essential for yield estimation and perception module of automated harvesting, but existing deep learning techniques often require substantial computational support, restricting their deployment on edge devices. This study addresses this gap by evaluating the Faster Objects, More Objects (FOMO) model—a lightweight architecture for resource-constrained platforms—for brinjal detection under diverse field conditions. A dataset of 1500 images was captured under varying illumination and growth stages, annotated using a bounding box-based approach, and used to train FOMO models with 25, 50, and 100 epochs via transfer learning. Post-training quantization to INT8 format was applied to assess improvements in computational efficiency. The Float32 model achieved a precision of 0.827, recall of 0.915, and F1-score of 0.869 at 100 epochs. The INT8-quantized model maintained comparable accuracy (precision 0.829, recall 0.908, F1-score 0.866) while reducing model size by 63.33% (from 0.30 MB to 0.11 MB), inference time by 62.02% (from 65.3 ms to 24.8 ms), and RAM usage by 73.02% (from 887.2 KB to 239.4 KB). These results demonstrate that FOMO combined with INT8 quantization provides an efficient, accurate solution for real-time brinjal detection on edge platforms, supporting the advancement of precision agriculture through intelligent crop monitoring and serving as a perception module for future robotic harvesting systems.

Automation doi: 10.3390/automation7040119

Authors: Hanngyoo Kim Seunghwan Lee

Occupancy map merging in dynamic environments is challenging because moving objects introduce time-varying disturbances that degrade registration accuracy and structural consistency. This paper proposes a reliability-aware map-merging method that explicitly models temporal validity and probabilistic reliability in occupancy grid maps. The method constructs two complementary maps: a trajectory-guided temporal reliability map that reflects the recency and persistence of cell observations and a probabilistic reliability map that refines detected candidate regions based on temporal persistence and spatial reliability criteria. By suppressing transient clutter before registration, the proposed approach focuses alignment on structurally stable regions and improves merging robustness. Experiments in simulation and real-world indoor environments demonstrate clear improvements over a conventional feature-based baseline, substantially reducing both translation and rotation errors. These results show that incorporating temporal validity and probabilistic reliability can improve occupancy map merging under dynamic conditions.

Automation doi: 10.3390/automation7040118

Authors: Hassan A. Jeiad Sama S. Samaan Omar Janeh Saja D. Khudhur Amjad J. Humaidi

Plant diseases have long been considered a major threat to global food production systems. Therefore, early diagnosis is vital to mitigate the risk of these diseases. This task can be challenging, as the number of harmful diseases is substantial. One technology that has gained widespread interest is artificial intelligence, specifically deep learning, which is used to identify plant diseases using leaf patterns. This paper presents two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture. Experiments were carried out using the PlantCity dataset, which consists of twelve subsets of diverse crop species representing fruits, vegetables, and grains with variations among the subsets, including the number of classes, the subset sizes, class distribution, and visual complexity of disease symptoms. The two models were evaluated using multiple metrics, including accuracy, loss, precision, recall, and F1-score. Explainable AI using the LIME technique was deployed to better interpret the acquired results. Results showed that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model, with accuracies ranging from 86% to 99% across eleven experimented crops. In order to perform an independent experimental validation for the developed model, future work will focus on constructing a local crop dataset captured from Iraqi fields to evaluate the developed models based on the local environment.

Automation doi: 10.3390/automation7040117

Authors: Abdullah S. Hamoud Mahmood Farhan Mosleh Salah Al-Zubaidi Ramiz M. Shubbar

This paper presents a hybrid physics–AI framework for real-time emission monitoring in industrial boiler systems within an IIoT-enabled Industry 4.0 environment. The proposed framework integrates physics-based emission estimation with AI-based anomaly detection within a unified operational technology and information technology (OT–IT) architecture to support continuous environmental monitoring. Process data, including fuel oil consumption, oxygen concentration, temperature, and pressure, are acquired from an industrial boiler through a Siemens programmable logic controller (PLC) using an Open Platform Communications Unified Architecture (OPC UA) communication layer. The acquired measurements are processed at the edge analytics level to estimate the emission rates of carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter (PM) using stoichiometric combustion models based on fuel composition and flue gas characteristics. An autoencoder-based anomaly detection model is employed to identify abnormal operating conditions by monitoring the reconstruction error against a predefined threshold. The framework is validated using a PLC-based quasi-real-time prototype that replays one year of historical industrial boiler operating data. The emission estimation results show close agreement with reference engineering calculations, with relative errors below 0.1% across the evaluated operating conditions. The anomaly detection model achieved an F1-score of 96.14% and an AUC of 0.981. An edge monitoring dashboard provides real-time visualization of process variables, estimated emissions, and alarm status, while cloud connectivity supports remote monitoring and long-term data analytics. Overall, the proposed framework demonstrates how existing industrial process data can be utilized to transform conventional offline emission estimation into a continuous OT–IT monitoring service for legacy industrial environments.

Automation doi: 10.3390/automation7040116

Authors: Sajjad Karami Payam Teimourzadeh Baboli Christian Becker

The electrification of residential demand through electric vehicles (EVs), heat pumps (HPs), photovoltaic (PV) systems, and battery energy storage systems (BESSs) creates new congestion challenges in low-voltage (LV) grids. This study evaluates a transparent, deterministic, and real-time-capable rule-based energy management system (EMS) for curative thermal congestion management within a §14a EnWG-oriented setting. The EMS is implemented in MATLAB/Simulink and tested on a representative four-feeder LV network supplying 56 households. Congestion is detected from maximum phase root-mean-square currents using conservative transformer and feeder thresholds. After a threshold is reached, the EMS first activates available BESS support and then applies simultaneous feeder-wide EV limitation, batched round-robin curtailment, or staged feeder-wide reduction toward 4.2 kW. In the uncontrolled case, the Feeder 3 and transformer overload areas are 62.84 Ah and 48.50 Ah, respectively. All controlled scenarios remove at least 98.70% of the feeder overload and eliminate the transformer overload within the reported numerical precision. The batched strategy requires 328.54 Ah of cumulative feeder-current reduction, compared with 977.34 Ah for simultaneous control and 816.00 Ah for staged control, and achieves the highest feeder-relief efficiency. It therefore provides a balanced trade-off between congestion relief and intervention intensity for the investigated deterministic case.

Automation doi: 10.3390/automation7040115

Authors: Nagadeepan Anbazhagan Senthilkumar Vagheesan K. K. Ilavenil

Advanced aerospace alloys such as titanium alloy (Ti-6Al-4V) are widely employed in critical applications owing to their excellent strength-to-weight ratio and corrosion resistance. However, machining these alloys remains challenging due to significant tool wear, poor material removal rates, and surface integrity concerns. This research offers a sustainable and intelligent machining framework for a 3 mm thick Ti-6Al-4V alloy, utilizing coated wire electrical discharge machining (WEDM) linked with Multi-Agent Deep Reinforcement Learning (MADRL). A Box–Behnken experimental design was adopted to gather baseline data for pulse-on time, pulse-off time, servo voltage, and peak current. The MADRL architecture combines cooperative agents to improve MRR, surface roughness, and kerf width concurrently. Beyond performance increase, the sustainability parameters of energy consumption, dielectric fluid use, and wire consumption were also studied. The proposed MADRL significantly improved the material removal rate (MRR) from 1.00 to 1.28 mm3/min and reduced the average surface roughness (Ra) from 1.95 to 1.66 µm, the kerf width from 0.27 to 0.24 mm, and the energy consumption from 122 to 107 J. The results show the potential of the proposed framework for adaptive, sustainable, and high-performance aerospace manufacturing.

Automation doi: 10.3390/automation7040114

Authors: Emmanuel García-Galvan Antonio J. Cruz-Estrada Eduardo Vincent-Islas José R. Rivera-Ruiz Edson E. Cruz-Miguel Javier Calderón-Sánchez José R. García-Martínez

High-precision X–Y positioning systems driven by stepper motors are widely used in industrial automation, manufacturing, and scientific instrumentation. However, fixed feedforward–feedback controllers may degrade when operating conditions vary, particularly as step frequency changes and the risk of synchronism loss increases. This work proposes an adaptive fuzzy feedforward–feedback controller for stepper-motor-driven X–Y positioning systems. The controller uses a Takagi–Sugeno (T–S) fuzzy inference system to adjust the proportional, derivative, and feedforward actions according to the tracking error, step frequency, and an auxiliary error-based adaptation variable. The control law is integrated with the inverse kinematics of the platform to generate synchronized step-domain commands, and a practical synchronism-preservation condition is established. Experimental validation on a NEMA 17-based X–Y platform showed accurate trajectory tracking, with a steady-state error of approximately 1.6[μm] for a trapezoidal profile. For a multi-segment trajectory, the RMSE was 0.0749[mm] without load and 0.0760[mm] under a 7.5[kg] external load. Compared with a conventional PID controller, the proposed method reduced the RMSE from 0.1741[mm] to 0.0749[mm], while preserving motor synchronism.

Automation doi: 10.3390/automation7040113

Authors: Eugenia Naranjo Juan Diego Erazo Rodríguez Iván Sinaluisa Nestor Ulloa

Urban traffic congestion remains a major barrier to sustainable mobility, necessitating a transition from static signaling to Intelligent Traffic Light Systems (ITLS). This study presents a systematic review in computer vision, artificial intelligence, and adaptive control algorithms for urban intersection optimization. Following a methodological framework informed by PRISMA guidelines, the study examines state-of-the-art architectures, including deep learning-based perception models and reinforcement learning agents. The findings indicate that, although two-stage detectors provide benchmark accuracy for vehicle perception, single-stage models and Vision Transformers offer the high-speed processing required for real-time traffic management. In addition, deep reinforcement learning enables autonomous, lane-specific optimization that outperforms traditional actuated and fixed-time systems. The review also identifies a persistent research gap in the deployment of these computational frameworks in the resource-constrained and heterogeneous infrastructures of developing countries. For the urban context of Riobamba, Ecuador, a phased implementation strategy is proposed that balances computational demands with the city’s morphological and social characteristics. By bridging the gap between high-fidelity simulation and practical field deployment, this review provides a scalable framework for improving throughput, reducing emissions, and enhancing safety. Overall, these advances offer a promising pathway toward more resilient transportation systems in rapidly evolving Andean urban centers.

Automation doi: 10.3390/automation7040112

Authors: Mukhtar Fatihu Hamza

This study presents an adaptive sliding mode control approach for trajectory tracking of a quadrotor unmanned aerial vehicle functioning under external interference and kinematic unpredictability. Linear and rotational motion equations formulated in inactive local coordinate system frames are developed with the aid of a nonlinear six-degrees-of-freedom quadrotor dynamic model. Through mitigating excessive switching activity, reliability is improved. Here, the proposed controller integrates sliding mode control with bounded adaptive switching gain factors and boundary-layer smoothing. The operational design is applied within a sequential outer-loop/inner-loop structure for linear and orientation control. The conventional sliding mode control, alongside the proportional derivative control, which employs MATLAB/Simulink R2024a simulations while being interference-affected with an unknown performance set-up, is deployed in this work to relatively appraise the proposed ASM controller. The assessment involves three-dimensional trajectory, control input characteristics, tracking error analysis, adaptive gain growth, and chattering analysis with quantitative performance metrics. The computational output revealed that the proposed ASMC attained superior tracking performance with limited oscillation and level control action. The controller achieves a total RMSE of approximately 0.38 m and a lower aggregate tracking error when using the conventional SMC and PD controllers under equivalent conditions. Furthermore, the adaptive gain mechanism successfully lowers chattering while maintaining robustness against interferences, a large amount of ambiguity, and inertial imbalance with signal noise. The results validate that the proposed ASMC delivers a functional balance between robustness, control smoothness, and tracking accuracy alongside execution homogeneity for autonomous quadrotor UAV trajectory tracking in unsettled and unstable environments.

Automation doi: 10.3390/automation7040111

Authors: Alexandr Zolotov Tursynkhan Zhylkybayev Dinara Kozhakhmetova Yerbol Ospanov Bakhytgul Kopabayeva Rashid Nazarov Dmitriy Myassoyedov Tatyana Ustinova Kulken Zenkovich Ahmet Sakir Dokuz

In the context of water scarcity and tightening environmental requirements, improving the energy efficiency of biological wastewater treatment processes has become particularly important. The aeration tank is one of the most energy-intensive and dynamically complex units, strongly affected by the variability in influent flow and composition. Conventional PID controllers do not provide predictive disturbance compensation and often result in excessive aeration and increased energy consumption. The study proposes an intelligent control approach based on a digital twin, neural network-based influent flow forecasting, and model predictive control (MPC). The digital twin represents a dynamic model of the biological process incorporating key state variables, including substrate, activated sludge, and dissolved oxygen concentrations. The LSTM neural network model is employed to predict the hydraulic load based on historical plant operation data, as well as to compensate for residual nonlinear dynamics that are not represented by the linearized MPC model. The predicted influent flow values are incorporated into the MPC framework as measured disturbances, enabling the generation of anticipatory control actions for the aeration system. The adequacy of the digital twin was validated using operational data from the wastewater treatment facilities of Semey city and was characterized by RMSE = 0.14 mg/L, MAE = 0.09 mg/L, R2 = 0.94, and MAPE = 6.3%. The simulation results demonstrated that, compared with the fuzzy PID controller, the application of MPC reduced the RMSE by 57.1%, decreased the overshoot from 24% to 8%, reduced the integral absolute error (IAE) by 60.9%, and lowered the energy consumption of the aeration system by 21.1%, while maintaining the dissolved oxygen concentration within the permissible operating range. The proposed Advisory MPC architecture is compatible with existing PLC–SCADA systems and can serve as a basis for the gradual digital modernization of wastewater treatment facilities without modifying the existing automation loops.

Automation doi: 10.3390/automation7040110

Authors: Sebastián Salazar-Méndez José Daniel Pico-Díaz Oscar Danilo Montoya

The increasing penetration of direct-current (DC) technologies in power transmission and distribution systems necessitates efficient and robust tools for steady-state analysis. This paper presents a comparative evaluation of the Chebyshev–Halley (CH) family of multipoint iterative methods against the classical Newton–Raphson (NR) method for power flow calculation in monopolar DC networks. Both methods were implemented in MATLAB and tested on four radial test systems of increasing complexity (10, 21, 33, and 69 nodes) under three distinct initialization scenarios: optimal (flat start), adverse (V(0)=0.5 p.u.), and random (V(0)∼U[0.8,1.2] p.u.). Performance was assessed using key metrics including iteration count, CPU time, solution accuracy, and convergence failure rate. The results demonstrate that the cubic convergence of CH consistently reduces the number of iterations by one when compared to NR across all systems. However, this reduction does not translate into computational savings, as CH exhibits median CPU times 1.36 to 2.44 times higher than those of NR, given its higher cost per iteration, which involves solving two additional linear systems. Under adverse starting conditions, both methods converge for the 10-, 21-, and 33-node systems, but CH fails on the 69-node network due to severe Jacobian ill-conditioning, from which NR recovers through an implicit regularization mechanism. Under random initializations, both methods show high failure rates, reaching 100% in the 69-node network. It is concluded that, while CH offers superior convergence order and final accuracy, NR remains more computationally efficient for small- to medium-scale networks under flat-start conditions. The CH family is best justified in high-precision applications or larger networks where the iteration reduction may offset its per-step overhead. Future work should focus on extending CH to meshed and multi-source DC networks, developing quasi-Newton variants to reduce its computational cost, and designing hybrid NR-CH strategies that combine global robustness with local cubic convergence.

Automation doi: 10.3390/automation7040109

Authors: Baha M. Hasan Jan Wikander Mauro Onori

This paper presents a semantic approach to support knowledge sharing in the assembly domain. Specifically, it focuses on capturing and sharing assembly design knowledge and on integrating the assembly design domain with the Assembly Process Planning (APP) domain through ontological modeling. A multilayered, heavyweight ontology framework, called the Product-Assembly Planning Ontology (PAPO), is proposed to integrate product assembly and APP. The ontology is based on product assembly features and uses these design features to provide the high-level semantic knowledge necessary to integrate product assembly design with APP. The paper also describes a detailed methodology for ontology design. Additionally, a rule-based engine is developed to reason about the available assembly design and APP knowledge and to infer new knowledge from them. Case study examples are included to illustrate the approach.

Automation doi: 10.3390/automation7040108

Authors: Rizwan Raza Zahoor-ur-Rehman Muddasar Naeem Farhan Aadil Faheem Shehzad Antonio Coronato

Flying Ad-Hoc Networks (FANETs), Internet of Things (IoT), and Internet of Vehicles (IoV) are critical enablers of intelligent transportation and smart city ecosystems. Their reliance on shared wireless channels, however, exposes them to diverse jamming attacks that threaten communication reliability, mission effectiveness, and safety. This paper presents a comprehensive study of jamming threats in integrated FANET–IoT–IoV environments and analyzes conventional and advanced anti-jamming techniques across physical, link/MAC, spectral, spatial, temporal, and hybrid domains. To address the challenges posed by heterogeneous and dynamic network conditions, we propose a cross-layer anti-jamming framework that integrates Cognitive Radio (CR) for dynamic spectrum access and Multi-Agent Reinforcement Learning (MARL) for cooperative, adaptive decision-making. The framework employs a Perception Engine for local anomaly detection, a Cognitive Engine for constructing a collaborative jamming map, and a Decision and Action Engine for multi-agent DRL-based mitigation. Simulation results demonstrate that the proposed CR-MARL framework significantly improves packet delivery ratio, reduces latency, and adapts efficiently to varying jamming strategies, while maintaining low energy and computational overhead, making it suitable for resource-constrained UAVs, vehicles, and IoT sensors.

Automation doi: 10.3390/automation7040107

Authors: Ho Cuu Leminh Huynh Žarko Koboević

Reliable communication infrastructure is a fundamental component of Intelligent Transport Systems (ITSs), particularly in scenarios involving maritime corridors and emergency traffic management. In locations where optical fiber deployment is geographically constrained, unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay links provide a flexible and rapidly deployable alternative. However, atmospheric attenuation, turbulence-induced fading, and wind-induced UAV misalignment can severely degrade link reliability and disrupt real-time transport data streams. This study proposes a payload-efficient multiple-input multiple-output free-space optical (MIMO-FSO) relay architecture based on a multi-output/single-input (MOSI) uplink and a single-output/multi-input (SOMI) downlink. Here, MOSI denotes multiple ground-based transmit apertures directed toward a single UAV receiving aperture, whereas SOMI denotes one UAV transmitting aperture serving multiple ground-based receiving apertures. Unlike conventional symmetric UAV-assisted MIMO-FSO relays that may duplicate diversity hardware on the aerial node, the proposed design shifts the parallel optical branches to the ground stations and keeps only one optical receiver and one optical transmitter on board the UAV. Under the adopted 4 × 4 comparison assumption, this reduces the UAV-side optical branch count from eight to two, corresponding to a 75% branch-count reduction proxy. System performance is evaluated over a 1.54 km relay link. The analytical framework describes Beer–Lambert attenuation, log-normal/gamma–gamma turbulence, and statistical pointing errors; in the OptiSystem implementation, their combined effects are represented by equivalent aggregate losses of 25 dB/km for atmospheric absorption/scattering and 25.5 dB/km for turbulence- and pointing-related degradation. Comparative simulations for SISO, 2 × 2, and 4 × 4 configurations show that the proposed 4 × 4 architecture increases the Q-factor from 8.38 to 18.25 and changes the OptiSystem-reported minimum BER from 2.73 × 10−17 to 9.95 × 10−75. Because a finite simulation cannot statistically validate error probabilities of this magnitude through raw error counting, values far below 10−12 are interpreted primarily as comparative indicators of receiver decision margin. The findings provide simulation-based evidence that the proposed architecture is a scalable candidate for resilient optical wireless backhaul in smart transport corridors under adverse propagation conditions.

Automation doi: 10.3390/automation7040106

Authors: Viktor A. Vedeneev Viktor V. Kondratiev Konstantin V. Suslov Roman V. Kononenko Galina Yu. Vitkina Vitaliy A. Gladkikh Yulia I. Karlina Antonina I. Karlina

This paper presents a single-company field study on hybrid user memory filtering for Large Language Model (LLM)-based knowledge management systems, aiming to reduce contextual noise from irrelevant or outdated persistent memories. We propose the Hybrid Adaptive Filtering Engine (HAFE), which combines intent classification, ontology-based filtering, behavioral reuse prediction, and collaborative role-level comparison. HAFE was integrated into an industrial KM platform deployed at a major steel producer. In a field experiment with 120 engineers, HAFE reduced irrelevant memory retention by 41%, improved Mean Reciprocal Rank (MRR) by 12.5%, and increased user satisfaction (SUS score) by 18% (all p < 0.01). The results suggest that proactive memory quality control can improve effectiveness and user experience in this specific industrial KMS setting, while further cross-domain validation is required.

Automation doi: 10.3390/automation7040105

Authors: Melina Athanasiadou Giovanni Franzini Adrien Metge

Manned–unmanned teaming (MUMT) represents a critical evolution in collaborative operations across domains including search and rescue, firefighting, surveillance, and defense. Despite widespread interest in MUMT capabilities, the field lacks a unified taxonomy for classifying and comparing system capabilities, hindering systematic development and technology integration. This paper presents a comprehensive framework for MUMT that addresses the fundamental challenge of organizing and assessing cognitive agent capabilities within human–machine teams. Building upon established automation frameworks, we propose a three-dimensional framework comprising information analysis and inference, decision-making, and action execution. Each dimension defines six hierarchical levels of teaming, ranging from human-only operations to fully autonomous cognitive agent capabilities. The framework distinguishes itself from existing taxonomies by explicitly modeling collaborative teaming rather than simple task delegation, incorporating transparency requirements, and addressing dynamic authority relationships between humans and cognitive agents. The proposed taxonomy provides researchers and engineers with a common vocabulary for MUMT development, enables gap analysis for technology roadmaps, and facilitates the identification of integration opportunities across organizational boundaries.

Automation doi: 10.3390/automation7040104

Authors: Viktor A. Vedeneev Viktor V. Kondratiev Konstantin V. Suslov Roman V. Kononenko Aleksey S. Govorkov Vitaliy A. Gladkikh Yulia I. Karlina Antonina I. Karlina

In the context of industrial digitalization and the widespread adoption of process automation systems, Knowledge Management Systems (KMS) play a key role in providing operational personnel with up-to-date instructions and regulations. However, the inherent ambiguity of natural language in technical documentation remains a serious obstacle, leading to incorrect operator actions, process deviations, and increased safety risks. This article investigates the integration of Large Language Models (LLMs) into KMS and its impact on user experience and human–machine interaction in industrial automation environments. A method called Semantic Latent Choice Detection is presented, designed to systematically identify interpretation ambiguities in process instructions and operator commands. Unlike existing approaches that require access to the internal model architecture (“white box”) or token-level logits, the proposed method is logit-free and operates with closed commercial LLMs (“black box”) via standard API interfaces. The method analyzes the semantic similarity of binary text blocks and polysemous terms within the context of a specific technological process. Using a metallurgical production case study, we demonstrate how the system detects hidden semantic collisions (e.g., the difference between “adding ferroalloys into the ladle” and “feeding ferroalloys onto the conveyor”) that are missed by traditional rule-based validation methods. Instead of arbitrarily selecting an interpretation, the system initiates a clarification request to the human operator, thereby reducing cognitive load, preventing erroneous automated decisions, and increasing trust in the KMS. An empirical evaluation conducted in a real-world industrial setting (unit control rooms and dispatch centers) shows a statistically significant reduction in errors related to misinterpretation of process regulations. The article contributes to the fields of automation engineering, knowledge management, and human-centered automation by proposing a novel method for validating operational instructions in high-risk industrial environments.

Automation doi: 10.3390/automation7040103

Authors: Israel Galhardo José Antonio de Queiroz José Henrique de Freitas Gomes

Engineer-to-Order (ETO) manufacturing environments are characterized by high product variety, low repetitiveness, and unstable demand, which pose significant challenges to the application of Lean Manufacturing (LM). This study investigates the application and adaptation of LM principles and tools in an ETO production line using an action research approach integrated with Value Stream Mapping (VSM). The research was conducted at a manufacturer of highly customized electrical equipment. An adapted method for calculating representative cycle times based on weighted production volumes was developed to support line sizing and workload balancing. The proposed future-state design incorporates multifunctional operators, FIFO lanes, daily scheduling, and pitch-based control. The results show a 9.5% reduction in labor requirements, a 61.7% decrease in manufacturing lead time, and a 75.0% reduction in overtime hours. Statistical validation using daily PPC records confirmed significant improvements in actual output, schedule adherence, overtime, and lead time after implementation. In addition to operational improvements, this study offers methodological contributions by proposing practical adaptations of LM tools suitable for high-variability ETO environments, thereby contributing to both theory and industrial practice.

Automation doi: 10.3390/automation7040102

Authors: Fatma Zohra Latrech Asma Ben Rhouma Adel Khedher

The control of Dual-Star Induction Machines (DSIMs) with high performance remains a challenging task, particularly in the presence of parameter variations and under sensorless operation. In practice, widely used controllers such as Proportional–Integral (PI) and classical sliding mode (SM) often reach their limits, especially in terms of dynamic responses, sensitivity to disturbances, and chattering, which can negatively affect system stability and efficiency. In this work, an improved Rotor Flux-Oriented Control (RFOC) strategy is proposed. It combines a super-twisting sliding mode (STSM) speed controller with a Model Reference Adaptive System (MRAS) observer. The STSM controller ensures faster convergence and enhanced robustness while significantly reducing chattering. Meanwhile, the MRAS observer enables accurate rotor speed estimation without mechanical sensors, thereby simplifying the system and improving reliability. The control scheme is developed using the Xilinx System Generator (XSG) in a fixed-point environment, providing an FPGA-oriented and compatible simulation framework. To assess its effectiveness, the proposed method is evaluated through several simulation scenarios and compared with conventional RFOC-PI and RFOC-SM approaches. The results demonstrate clear improvements in dynamic performance, disturbance rejection capability, and steady-state accuracy. Overall, the proposed approach provides a practical and efficient solution for DSIM drive systems operating under demanding conditions.

Automation doi: 10.3390/automation7040101

Authors: Rafael Rojas-Galván Luis F. Olmedo-García José R. García-Martínez José Manuel Alvarez-Alvarado Ricardo Rojas-Galván Juvenal Rodríguez-Reséndiz

Learning-based approaches have significantly advanced the capabilities of Simultaneous Localization and Mapping (SLAM) systems, particularly in challenging environments characterized by noise, dynamic objects, and perceptual ambiguity. However, the literature remains highly heterogeneous in terms of sensing modalities, datasets, evaluation protocols, and reporting practices, making systematic comparison difficult. This paper presents a taxonomy-driven review of learning-based SLAM approaches, with particular emphasis on LiDAR-based systems in mobile robotics, and introduces a functional taxonomy that categorizes methods according to the role of learning within the SLAM architecture: (i) learning-enhanced front-end SLAM (T1), (ii) learning-enhanced back-end SLAM (T2), and (iii) learning-centric SLAM systems (T3). Representative studies were analyzed with respect to performance characteristics, robustness, computational requirements, datasets, and deployment-related evidence. The analysis shows that T1 approaches primarily improve local pose estimation and robustness, T2 methods enhance global consistency through learning-based loop closure and relocalization, and T3 approaches explore unified representations, semantic reasoning, and learning-centric autonomy, albeit with greater computational demands and limited deployment evidence. The review further indicates that hybrid approaches combining geometric and learning-based components constitute a prominent trend in the literature, frequently reporting improvements in accuracy and adaptability while maintaining compatibility with established SLAM frameworks. Nevertheless, these observations should be interpreted cautiously, as stronger empirical evidence for hybrid systems may partially reflect their greater technological maturity and broader evaluation history. Finally, the review identifies persistent challenges, including limited cross-domain generalization, high computational requirements, limited deployment-oriented evaluation, and the lack of standardized benchmarking and reporting practices. These findings highlight the need for more reproducible evaluation methodologies, uncertainty-aware learning strategies, and computationally efficient architectures for robust real-world autonomous SLAM.

Automation doi: 10.3390/automation7040100

Authors: Ying Zhou Yixin Feng Peiyan Mao Pengfei Wang

A common limitation of existing multi-AGV cooperative systems is their reliance on the obstacle-agnostic Manhattan distance as the basis for reward signals. This causes agents to receive misleading feedback, engage in excessive futile exploration, and ultimately achieve poor training quality. To address this, we introduce an A*-distance guidance mechanism for multi-agent reinforcement learning (MARL) path planning, built on the precise path distance computed via the A* algorithm (A*-distance). Within the QMIX framework, we incorporate an A*-distance-based guiding function into the action selection mechanism. This function evaluates candidate actions by quantifying their immediate effect on the A*-distance, providing positive incentives for actions that bring the agent closer to the goal and applying negative penalties for those that lead it farther away. This effectively biases exploration towards actions that genuinely shorten the obstacle-aware path to the goal, suppresses ineffective exploration, and accelerates policy convergence. Experiments in four warehouse environments (simple obstacles, complex obstacles, large-scale, and congested) show that, compared with standard QMIX, the proposed method achieves higher global average reward and faster convergence. The advantage grows as environment scale and obstacle density increase. In the large-scale and congested environments, standard QMIX and the other MARL baselines fail to solve the task, whereas the proposed method still succeeds. It is the only learning-based method to solve these hardest tasks while keeping path length close to that of dedicated search-based solvers. Ablation experiments further show that the A*-distance-guided action selection is the primary contributor to these gains, while the A*-distance reward plays a supporting role.

Automation doi: 10.3390/automation7030099

Authors: Diogo Marta Bernardo Firme Miguel S. E. Martins João M. C. Sousa Susana M. Vieira

This paper proposes a genetic algorithm (GA) for the Dual-Resource Flexible Job Shop Scheduling Problem with Partial Resource Allocation (DRFJSSP-PRA), a particular variant of a dual-resource constrained scheduling problem that has not been fully explored due to its intricate nature. The DRFJSSP-PRA poses a challenging scheduling problem, having several applications in many industries, including food, chemistry and pharmaceutics. The proposed algorithm is applied to real-world scheduling instances in pharmaceutical quality control. The objective function considered is the total completion time. The GA is compared with three state-of-the-art algorithms. For small- and medium-size instances, the proposed algorithm achieves optimal or near optimal results for the majority of the instances tested. For large-sized instances, the proposed GA outperforms all the other algorithms, in all of the tested instances. Thus, the experimental results show that the proposed GA achieves competitive results for any type of instance. The proposed algorithm also has the ability to optimize production processes through scheduling, leading to potential cost savings, increased efficiency, and improved competitiveness.

Automation doi: 10.3390/automation7030098

Authors: Zohra Zidane El Mostafa Atify Mohammed Zidane Ahmed Boumezzough

Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing process control; therefore, the liquid level in a tank must be maintained at a precise reference point. This is where liquid level control for tanks becomes crucial and constitutes a fundamental problem in the industrial sector due to nonlinearities, multivariable coupling, and stochastic disturbances. Given the drawbacks of available control methods, such as classical Model Predictive Control (MPC), which are highly dependent on model accuracy and struggle to reject complex stochastic noise, predicting random disturbances represents a major technological challenge. A new approach is proposed to specifically address the problem and challenge of the four-tank system, where water levels in two lower tanks must be controlled by two pumps, often with varying delays and significant parameter disturbances. To establish a relationship between expected performance and MPC parameters, this approach uses a novel hybrid nonlinear MPC, Extended State Observer, and Physics-Informed Neural State Estimation (NMPC-ESO-PINSE) architecture. A Physics-Informed Neural State Estimation (PINSE) layer, chosen for its learning capacity, is designed to filter sensor noise by applying Bernoulli’s physical laws, while an Extended State Observer (ESO) is integrated to capture and compensate for unmodeled uncertainties in the process. Finally, a proposed hybrid (NMPC-ESO-PINSE) strategy leverages these clean, physically consistent state estimations to solve a non-convex optimization problem via Sequential Quadratic Programming (SQP), computing optimal pump voltages. Extensive numerical simulations demonstrate the superior resilience of this decoupled framework against parametric drifts and continuous noise sequences, yielding a +27.36% reduction in global Root Mean Square Error (RMSE) compared to standard NMPC, accelerating the closed-loop settling time to 15.2 s, and restricting transient overshoot to just 0.18%.

Automation doi: 10.3390/automation7030097

Authors: Xueqi Yuan Qing Sun

This paper focuses on state estimation for nonlinear stochastic systems with multiple switching models, especially under challenging conditions where the model dynamics are unknown and the transition probability matrix is uniformly distributed. Gaussian process regression is employed to learn the unknown system dynamics from an offline discrete dataset and is integrated into an interacting multiple model particle filtering framework. GPR enables data-driven learning of both state transition and observation functions. To cope with model uncertainty and uninformative prior transition knowledge, particularly under uniformly initialized TPM, a dual-layer adaptive TPM update strategy based on hidden Markov model inference is further incorporated. Finally, the proposed method is validated through simulations and compared with IMMPF under different assumptions on system dynamics and TPMs. The results show that, even without prior knowledge of the system dynamics or precise TPM information, the proposed GP-AIMMPF maintains robust and accurate state estimation performance.

Automation doi: 10.3390/automation7030096

Authors: Maral Niazi Hossein Hassani Madison Lee

AI-enabled cyber–physical systems (CPSs) are increasingly deployed in public governance contexts where they sense human populations, infer classifications or risks, and trigger interventions that can shape liberty, equality, and access to essential services. In these deployments, governance failures often arise not only from model error but from systems-level interactions across data generation, model updates, organizational practices, and downstream actuation. This paper introduces a Risk–Rights–Rules (3R) architecture that treats fundamental rights and legal rules as enforceable constraints on the sensing–inference–actuation loop, rather than as external ethical aspirations. Building on established risk-management baselines and safety engineering practice, we specify a testable assurance object, a structured 3R assurance case, that links rights claims to explicit assumptions, measurable evidence, and accountable control points across the lifecycle. The approach is designed to reduce “legitimacy drift” in stochastic decision pipelines by making uncertainty, demographic error, contestability, and procurement leverage auditable at the system level. The result is a governance blueprint for high-consequence public-sector AI deployments for governance failures, which is both technically robust and institutionally defensible.

Automation doi: 10.3390/automation7030095

Authors: Fahad Alotaibi Habib Dhahri Saleh Almohaimeed Awais Mahmood

Background/Objective: Autonomous vehicles (AVs) require control algorithms capable of handling complex and dynamic environments while satisfying multiple conflicting objectives such as safety, comfort, energy efficiency, and trajectory accuracy. Model predictive control (MPC) offers a principled framework for multi-constraint optimization, yet its real-time feasibility remains challenging for nonlinear vehicle dynamics. Methods: This paper presents a feedforward neural network (FNN)-based MPC framework for autonomous vehicle trajectory tracking. The FNN approximates the coupled vehicle dynamics and visual preview error model using an algebraic sum of log-sigmoid functions. Three adaptive FNN parameter sets, namely, the scaling factor, convergence parameter, and time-shifting parameter, are jointly optimized using a hybrid algorithm that combines the global search capability of fractional particle swarm optimization (FPSO) with the local refinement of sequential quadratic programming (SQP). Results: Comprehensive scenario-based simulations are performed to evaluate trajectory tracking dynamics under dry conditions with an adhesion coefficient of 0.8 and a vehicle mass of 1723 kg moving at a speed of 80 km/h. The results are quantitatively compared with a traditional PID controller and a structurally comparable MPC framework from the literature under identical simulation conditions; related DRL- and RL-based methods are discussed qualitatively for contextual orientation only. The stability, reliability, and computational complexity of the proposed framework are examined based on the mean square error, fitness value, and computational budget in GFLOPs for 100 independent runs. Conclusions: The proposed FNN-based MPC framework demonstrates improved tracking accuracy and optimizer reliability in simulation. While the present results indicate promising computational behavior, real-time deployment will require further validation on embedded automotive hardware and under closed-loop real-time constraints.

Automation doi: 10.3390/automation7030094

Authors: Prajakta Salunkhe Mahesh Shirole Ninad Mehendale

Environmental monitoring in hazardous indoor zones conventionally relies on fixed-sensor networks or manual inspections, both of which suffer from spatial blind spots and increased human exposure risks. This paper addresses the problem of transforming sparse, mobile sensor measurements into spatially resolved risk assessments in GPS-denied environments. We propose a Hazard Index (HI) framework that normalizes environmental parameters against established safety thresholds into a unified, graduated risk metric with O(N) computational complexity, where N is the number of monitored parameters. The framework is designed for multi-parameter hazard assessment; the present work validates the computational pipeline, spatial mapping methodology, and classification logic through single-parameter CO2 detection (N=1) deployed on a LiDAR-guided robotic platform integrating an MQ-135 gas sensor interfaced via a NodeMCU ESP8266 microcontroller. Experimental validation across a 144 sq ft indoor area achieved a trajectory-following RMSE of 0.54 ft relative to planned waypoints using Hector SLAM without odometry, detected CO2 concentrations ranging from 0.02% to 0.25%, and identified a hazardous region encompassing eight measurement points (HI≥1.0) using a three-tier classification scheme (Safe, Elevated, Hazardous) within 225 s of active mapping. The framework provides a lightweight computational footprint suitable for real-time evaluation on an NVIDIA Jetson Nano. The proposed approach establishes a cost-effective, reproducible methodology for autonomous indoor environmental monitoring, with the modular architecture designed for future expansion to multi-parameter sensing.

Automation doi: 10.3390/automation7030093

Authors: Huaiyi Li Xiaogang Peng Wendi Li Yougen Liu Guolin Cai Hongxia Sun

Firefighter training requires accurate posture monitoring to reduce injuries and improve performance assessment, yet traditional tracking methods suffer from high occlusion rates and the uniform appearance of trainees. To address these challenges, we propose an improved multi-target tracking algorithm that integrates YOLOX for detection, BlazePose for posture estimation, and a pose-constrained extension of DeepSORT. First, posture features are introduced into the association metric through a posture-cosine distance, which enhances discrimination between visually similar firefighters. Second, a pose-guided bounding-box correction is applied to ensure complete coverage of the human body region, improving the quality of extracted posture information. Experiments were conducted on a custom firefighter training dataset comprising 6602 labeled images and five multi-target video sequences (FM-1 to FM-5). The proposed method achieved a mean Average Precision (mAP) of 97.8% for detection and improved tracking performance compared to baseline DeepSORT, with MOTA rising from 74.72% to 82.96% and IDF1 from 74.77% to 82.36%. These results demonstrate that the algorithm effectively handles severe occlusion and appearance similarity, providing a reliable tool for posture tracking and behavior perception in firefighter training environments.

Automation doi: 10.3390/automation7030092

Authors: Quy-Thinh Dao

This paper investigates uncertainty-resilient stabilization of an inverted pendulum on a cart (IPOC) using an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model and an LQR-based control framework. The IPOC dynamics are represented as a weighted combination of local linear subsystems, where interval firing strengths derived from upper and lower membership functions capture modeling uncertainties. An LQR state-feedback controller is designed for each subsystem, and the final control input is obtained by blending the local controllers according to the normalized firing strengths. To analyze stability, an LMI-based verification condition is established as a sufficient condition for the subsystem LQR controllers. Simulation results show that this condition is satisfied only in a limited operating region, while the closed-loop system can still remain stable even when the condition is violated, demonstrating the reduced conservatism and flexibility of the proposed approach. Furthermore, comparisons with the conventional PDC structure confirm that the proposed method provides greater design flexibility and enables a trade-off between robustness and transient-state performance.

Automation doi: 10.3390/automation7030091

Authors: Sini Sanal Ananthan Thangavelu

This paper proposes a reinforcement learning-assisted active disturbance rejection control (ADRC) framework for a nonlinear quadrotor unmanned aerial vehicle (UAV). Conventional ADRC controllers are designed for the quadrotor altitude and attitude channels. To evaluate robustness under disturbance-intensive conditions, a composite external disturbance is injected into the roll-channel dynamics. A Deep Deterministic Policy Gradient (DDPG)-based adaptive tuning mechanism is integrated into the roll-channel ADRC for the nonlinear state error feedback (NLSEF) gain adaptation, while fixed-parameter ADRC is retained for the remaining three channels. Without requiring system linearization and prior knowledge of disturbance models, the reinforcement learning agent learns an optimal gain adaptation policy directly through interaction with the nonlinear roll subsystem. Quantitative simulations demonstrate superior roll-axis disturbance rejection, leading to 90% faster settling time, the root mean square (RMS) control effort being reduced by 5.1%, and a 7.6% peak input suppression compared to conventional ADRC. The learning-based adaptation maintains comparable tracking accuracy across all channels while significantly improving transient recovery and control smoothness in the most disturbance-sensitive axis, validating selective reinforcement learning integration for robust nonlinear quadrotor flight control.

Automation doi: 10.3390/automation7030090

Authors: Khandoker Islam Ahmed Abu-Siada

Modern distribution networks increasingly face operational stress from variable demand and high penetration of distributed energy resources, challenging the adequacy of purely reactive protection schemes. This study addresses this challenge by enhancing a developed adaptive protection software platform with a Geographic Information System (GIS) driven predictive load forecasting capability to enable anticipatory protection coordination. The proposed framework integrates spatially resolved demand modeling, regulatory and planning constraints, and machine learning-based short- to medium-term load forecasting with a relay coordination and optimization engine. Forecasted load profiles are used as inputs to an optimization layer that proactively updates relay pickup and time delay settings to maintain selectivity and system security under predicted operating conditions. The approach is validated at laboratory scale using real Intelligent Electronic Devices (IEDs) interfaced with synthetic GIS-based network and load datasets. Experimental results indicate that incorporating forecast-informed settings improves coordination margins and reduces the risk of relay maloperation compared with reactive adaptive protection alone. The findings demonstrate that coupling GIS based constrained load forecasting with adaptive relay control can enhance protection performance in active distribution networks, supporting more resilient and forward-looking protection strategies.

Automation doi: 10.3390/automation7030088

Authors: Ali Mahmood Róbert Szabolcsi

Ensuring safety in autonomous vehicles (AVs) requires predictive control methods that can handle dynamic constraints, uncertain interactions, and real-time decision making. This review examines safety-oriented model predictive control (MPC) for AVs using a PRISMA-guided screening process. From 363 records published between January 2015 and March 2026, 101 peer-reviewed studies were selected for qualitative synthesis. The literature is organized into three domains: collision avoidance and risk mitigation, trajectory tracking and path following, and intersection and coordination tasks. Across these domains, MPC has evolved from nominal tracking and geometric avoidance toward risk-aware, robust, hierarchical, and learning-enhanced formulations. Unlike broader reviews on autonomous driving control, this review focuses specifically on safety-oriented MPC and compares the reviewed literature in terms of safety mechanisms, uncertainty treatment, validation practice, computational feasibility, and deployment limitations. The review shows that MPC remains one of the most versatile frameworks for AV safety, but the evidence base is weakened by heavy reliance on simulation, inconsistent safety metrics, limited validation under uncertainty, and uneven treatment of computational feasibility. The most promising directions are hybrid architectures that combine model-based safety guarantees with uncertainty-aware prediction, learning-assisted adaptation, and scalable coordination mechanisms.

Automation doi: 10.3390/automation7030089

Authors: Oleg Gasparyan Nerses Nersisyan Liana Buniatyan Ovsanna Ohanyan Mariam Darakhchyan Karlen Begoyan Davit Danielyan Mkrtich Harutyunyan

In the paper, a systematic treatment of sensitivity analysis of multivariable cont rol systems within the framework of the characteristic transfer functions (CTFs) method is given. The CTFs method (also called characteristic gain loci method) allows one to associate with an N-dimensional multi-input multi-output (MIMO) system a set of N independent single-input single-output (SISO) characteristic systems and thereby to reduce the analysis and design of a MIMO system to the analysis and design of N SISO systems. The formulas determining the sensitivity functions of the CTFs and the sensitivity vectors of the canonical basis axes to small variations of parameters of general type MIMO systems are derived. The relations between the sensitivity functions of the open-loop and closed-loop MIMO systems are established. Two illustrative examples are considered. The first of them concerns the sensitivity of a two-dimensional non-robust system with a large degree of skewness of the canonical basis axes. In the second example, the sensitivity of the control system of a hexacopter (a multirotor UAV with six rotors) to small degradations in motors efficiency is analyzed.

Automation doi: 10.3390/automation7030087

Authors: Diana Novak Yuriy Kozhubaev Dmitry Kazanin Roman Dorovskih Georgiy Molodtsov

The present article discusses the application of genetic algorithms (GA) for solving multi-criteria optimization (MCO) problems in underground mining. It has been demonstrated that GAs are highly effective in identifying Pareto-optimal solutions in scenarios involving multiple conflicting criteria, specifically the simultaneous minimization of equipment failure rate, energy consumption, and repair costs. The article presents the main approaches to solving MCO problems, a brief overview of the most popular algorithms, such as NSGA-II and SPEA2, and their improved versions. The proposed algorithm, implemented in Python 3.11 using the DEAP library, incorporates adaptive crossover, enhanced diversity preservation, and problem-specific initialization. Quantitative analysis shows that the proposed algorithm achieves a Hypervolume Indicator of 0.796, representing a 7.2% improvement over standard SPEA2, with an 18.3% reduction in Inverted Generational Distance (IGD), indicating superior convergence to the true Pareto front. The algorithm identifies optimal trade-offs between conflicting objectives—for example, a 15% reduction in energy consumption correlates with a 10% increase in failure rate—providing decision-makers with quantified insights for operational planning. The novel idea is the use of an adaptive crossover strategy, a composite diversity maintenance technique, and application-specific initialization—all of which have not been used before for optimizing underground mining machinery. A visual analysis of the results, employing a graphical representation of the Pareto front, confirmed that the proposed approach enables experts to make informed decisions based on production priorities.

Automation doi: 10.3390/automation7030085

Authors: Arthur Kniphoff da Cruz Christian Siemers Lorenz Däubler Ana Clara Hackenhaar Kellermann Jaine Mercia Fernandes de Oliveira

This work proposes a vendor-independent integration method for International Electrotechnical Commission (IEC) 61850 Manufacturing Message Specification (MMS) communication protocol into Programmable Logic Controller (PLC) systems that support an open network communication interface available for the PLC program. IEC 61850 is globally well accepted for electrical substation control, and the protocol MMS is used for integrating the electrical substation bay level into the station level, where the PLC orchestrates the process level of the substation and parallel processes. This method was created because most PLCs lines do not natively support any protocol of IEC 61850, although it often needs to be used for the control of electrical substations. For the development of the prototype presented in this paper, PLCs from the Siemens AG families S7-1500 and S7-410, which support open communication over Transmission Control Protocol/Internet Protocol (TCP/IP) with external systems, were used for validation. Different results regarding network communication and PLC program performance are presented in this paper. The implemented solution presents a meaningful implementation of the MMS application layer into the PLC program and was successfully validated with real industrial, single and redundant PLC systems.

Automation doi: 10.3390/automation7030086

Authors: Le Ba Chung Tran The Hung Nguyen Viet Tien Pham Chung Pham Huy Dang

This paper focuses on the development of a mobile robot-based security surveillance and target-tracking application that combines image-processing algorithms with the Navigation Stack in the robot operating system (ROS). The proposed approach integrates a 3D camera with the MobileNet-SSD object detection model to estimate the target’s three-dimensional spatial coordinates in real time. These coordinates are continuously transmitted to the ROS Navigation Stack as dynamic goal points, enabling the robot to perform path planning and target-following while maintaining a predefined safety distance and avoiding obstacles. The proposed solution has been validated on a real differentially driven wheeled mobile robot. Experimental results demonstrate smooth and stable robot motion, accurate maintenance of the desired following distance, and reliable static obstacle avoidance while continuously tracking the target. These outcomes confirm the effectiveness and robustness of the integrated system for vision-based navigation tasks in indoor environments.

Automation doi: 10.3390/automation7030084

Authors: David N. Donkor Kingsley A. Ogudo Vikash Rameshar

The research presented provides an overview of the latest progress in data-driven control methods used for industrial heating furnaces. Although the data-driven methodologies reviewed provide good performance metrics compared to conventional control strategies, they lack the integration of energy efficiency considerations into the controller design process. This research presents a comprehensive control design framework for a novel energy-efficient data-driven controller applied to an industrial heating furnace. It proposes a novel Hybrid Mamdani–ANFIS controller developed using real-time data from an industrial heating furnace. A novel ANFIS-based energy model is also presented in this work to evaluate the energy efficiency of the presented controller models. The results demonstrated that the proposed novel Hybrid Mamdani–ANFIS controller outperforms both the Fuzzy PID and conventional Fuzzy controller in terms of energy efficiency, achieving approximately 30% energy savings and exhibiting a faster disturbance response time. This study makes a considerable contribution to the field of control theory by synthesizing existing knowledge, addressing identified research gaps, and introducing a novel control design framework that enhances energy efficiency, robustness, and adaptability across a wide spectrum of control applications in industrial heating furnace systems.

Automation doi: 10.3390/automation7030083

Authors: Aurelian Popescu Claudiu Vasile Kifor Codrina Victoria Lisaru

Fuzz testing is a key verification technique for identifying robustness and cybersecurity weaknesses in automotive electronic control units (ECUs). However, conventional CAN-based fuzz testing suffers from extremely low acceptance rates because randomly generated frames often violate protocol constraints such as counters, check-sums, and state dependencies. This study addresses the test-preparation bottleneck by proposing an AI-assisted approach for automated identification of stable operational system states from Controller Area Network (CAN) traces. These states can serve as valid starting points for mutation-based and model-based fuzzing. CAN traces generated in a Hardware-in-the-Loop (HIL) environment were analyzed using multiple publicly accessible large language model (LLM) systems. The objective was to evaluate whether AI/LLM tools can (i) identify unique system states, (ii) compute dwell-time distributions, and (iii) derive state transition maps directly from raw CAN traces and DBC definitions. Additionally, we checked the possibility of these tools to analyze the quality of CAN communication (message cycle time). At the end of the study, we ran experiment tasks using CAN logs taken from a production car. Results show that AI-assisted analysis can extract operational states and transitions with varying levels of agreement with the deterministic baseline, supporting preparatory analysis during fuzzing test preparation. While performance varies across tools, AI support demonstrates strong potential for accelerating and assisting structured fuzz testing workflows.

Automation doi: 10.3390/automation7030082

Authors: Sergii Tierielnyk Valery Lukovtsev

Shipboard diesel–electric power systems (SDEPSs) are inherently vulnerable to transient instability owing to their compact, isolated, and low-inertia design. Their performance is considerably influenced by dynamic disturbances, which can lead to operational failures and accidents of varying severity. Therefore, this research addresses the critical challenge of transient stability enhancement in SDEPSs during significant dynamic disturbances. Recognizing that traditional automation and protection systems respond only after transient instability occurs, this study introduces an emergency preventive control (EPC) strategy that enables anticipatory control of SDEPS power sources to enhance transient stability. The proposed EPC system integrates hardware and software components to perform real-time monitoring and control based on forecasting system parameters, specifically the relative rotor angles of the power sources. The feasibility and effectiveness of the proposed system are validated through comprehensive computer simulations, demonstrating improvements in transient stability and system resilience by substantially reducing relative rotor angle deviations during the transient event. Overall, the proposed framework can be readily integrated into existing shipboard control architectures, offering an effective means to improve the safety of modern SDEPSs operating under dynamic conditions.

Automation doi: 10.3390/automation7030081

Authors: Hamed Nozari Zornitsa Yordanova

This research presents an integrated framework for operational planning of low-power robotic agricultural systems, which combines digital twins, uncertainty modeling with triangular fuzzy numbers, and multi-objective optimization in a coherent structure. The goal is to balance energy consumption, carbon emissions, operational delay, and crop yield under variable and uncertain field conditions. The proposed framework was evaluated using real and simulated data, various operational scenarios, and comparative analyses. The results showed that this approach reduced energy consumption from 248.6 to 191.5 kWh and carbon emissions from 132.4 kg CO2 to 96.8 kg CO2, while increasing crop yield from 148.7 to 178.4 kg/day, compared to the deterministic baseline model. Also, the use of digital twins improved the quality of decision-making in different scenarios by about 6 to 7 percent, and fuzzy modeling significantly increased the stability of results at higher levels of uncertainty. The findings show that the proposed framework can be an effective tool for sustainable, smart, and energy-efficient agriculture.

Automation doi: 10.3390/automation7030080

Authors: Abu Zahid Md Jalal Uddin Atahar Nayeem Touhid Bhuiyan

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead.

Automation doi: 10.3390/automation7030079

Authors: Jose Magallanes Styven Palomino Anthony Gutarra Elvis Jara Alegria

Experimental validation of dissolved oxygen (DO) control in aquaculture is often limited by biological variability, environmental disturbances, and hydrodynamic complexity, which hinder reproducibility and reliable performance assessment. To address this, the present work proposes a laboratory-scale, control-oriented platform that minimizes external disturbances and enables experimentation under consistent conditions, supported by a statistically grounded methodology for performance evaluation. The platform integrates industrial-grade instrumentation and automated control hardware, ensuring reliable operation and practical relevance. Oxygen demand is emulated through chemical deoxygenation using sodium sulfite, allowing experiments to start from consistent near-zero DO conditions. Within this framework, On–Off and discrete-time PID controllers are implemented as baseline strategies to illustrate the methodology. Their evaluation through standard performance metrics and confidence-interval criteria illustrates how the platform can support rigorous, repeatable assessment of control actions. Rather than aiming at optimal control design, the proposed approach offers a benchmark methodology for dissolved oxygen regulation studies, providing a reproducible basis that may guide future investigations.

Automation doi: 10.3390/automation7030078

Authors: Quanyan Zhu

A central goal of neuroeconomics is to understand how humans make decisions and how their neural processes interact during strategic situations. Game theory provides mathematical tools for modeling such interactions, with equilibrium concepts, most notably the Nash equilibrium, predicting stable patterns of behavior. Classical equilibrium analysis, however, treats cognition as a black box and assumes fully rational agents, whereas human decision making is shaped by bounded rationality, heuristics, and neural constraints. To bridge this gap, we investigate equilibrium behavior directly in the space of neurocognitive activity. Electroencephalogram (EEG) signals provide a high-resolution measurement of neural dynamics underlying attention, conflict monitoring, and evidence accumulation. In this work, we introduce a Neuronic Nash equilibrium, an equilibrium concept defined not in the action space but in the EEG-derived neural representation space. We develop a framework for analyzing two-player turn-based games in EEG space by constructing DMD-based neural embeddings and associated directed network representations. Dynamic Mode Decomposition (DMD) reveals statistically significant differences between the neural dynamics associated with distinct strategic actions, demonstrating that EEG-derived features preserve behaviorally meaningful cognitive structure. The resulting neuronic network representation enables equilibrium analysis directly at the neural level and provides a principled method for linking strategic behavior with stable patterns of neural activity. Our findings suggest that neural-state equilibrium concepts can capture the cognitive foundations of strategic interaction and offer a pathway toward characterizing cognitive equilibrium outcomes in multi-agent settings.

Automation doi: 10.3390/automation7030077

Authors: Janis Peksa Dmytro Mamchur

Digital Twins (DTs) have emerged as a key technology for sensor-driven cyber–physical systems, enabling such features as real-time monitoring, predictive maintenance, and operational optimization. Despite rapid progress, existing research in the area remains fragmented, mostly addressing only singular aspects, such as data acquisition, modeling, or control, lacking a unified lifecycle-oriented methodology capable of integrating heterogeneous sensor infrastructures, hybrid analytical models, and continuous feedback mechanisms. This paper presents a comprehensive state-of-the-art review of Digital Twin technologies, focusing on sensor-centric architectures, data integration strategies, and hybrid modeling approaches. Based on the identified limitations, a novel Federated Digital Twin Lifecycle Model (F-DTLM) is proposed as a unifying framework for industrial applications. The model structures the DT lifecycle into four iterative phases—Definition and Scoping; Sensor Data and Infrastructure Federation; Hybrid Modeling and State Synchronization; and Operational Optimization and Closed-Loop Control, supported by cross-cutting layers addressing interoperability and governance. The integration of federated sensing infrastructures with hybrid physics-informed and data-driven models enables scalable synchronization between physical and digital systems. A comparative analysis and an illustrative predictive maintenance scenario illustrate the potential applicability of the proposed approach.

Automation doi: 10.3390/automation7030076

Authors: Kohei Arai

High-rise LiDAR scanning produces vertically sparse point clouds where upper-layer defects are hardest to detect due to inverse-square ranging law (1/r2) density gradients, noise contamination, and complex geometries. This paper presents PC-TowerNet, a physics-aware AI pipeline that achieves state-of-the-art reconstruction through sequential modules: (1) 50D geometric feature classification outperforming CloudCompare SOR (100% accuracy vs. 91.3% retention); (2) Physics-Constrained VAE (PC-VAE) recovering 28.7 ± 2.1% upper density vs. 8.3 ± 1.7% standard VAE; (3) multi-modal PointNet++/GNN/Transformer fusion; and (4) Bayesian uncertainty maps (ECE = 0.042 ± 0.008). Synthetic tower evaluation (10 × 5 seeds) demonstrates 48.9% surface smoothness improvement and 38.2% volume error reduction over tuned RANSAC baselines, with clear paths to real-data validation.

Automation doi: 10.3390/automation7030075

Authors: Eduardo Vincent-Islas María I. Cruz-Orduña José R. Rivera-Ruiz Edson E. Cruz-Miguel Zayra E. Santos-Flores Ce Tochtli Méndez-Ramírez José R. García-Martínez

Adaptive cruise control (ACC) is a key component of advanced driver assistance systems, as it maintains a safe distance from preceding vehicles by regulating speed and spacing. However, vehicle dynamics, measurement uncertainty, and traffic variability pose significant challenges for conventional control methods. In this context, fuzzy logic (FL) has been widely explored for its ability to handle uncertainty and incorporate expert knowledge via linguistic rules. This article presents a systematic literature review on the application of FL in ACC systems, proposing a functional taxonomy based on the role of the fuzzy system within the control architecture. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 103 initial records were identified, of which 87 studies were included in the final analysis. Four main categories are defined: Direct Fuzzy Control/Learning-Based, Fuzzy Supervisory Decision Control, Fuzzy Adaptive Robust Control, and Fuzzy Model-Based Control. Results indicate that Direct Fuzzy Control/Learning-Based and Fuzzy Supervisory Decision Control dominate the literature, accounting for 35.6% and 28%, respectively, while Fuzzy Adaptive Robust Control and Fuzzy Model-Based Control represent 20.7% and 14.9%. Mamdani-type systems predominate (78.16%), followed by Takagi-Sugeno (T–S) systems (17.24%), while type-2 fuzzy systems remain limited (4.60%) due to higher computational complexity. Recent trends highlight growing interest in adaptive and robust FL-based strategies.

Automation doi: 10.3390/automation7030074

Authors: Mykola Nykolaychuk Leonid Zamikhovskyi Ivan Levitskyi Volodymyr Kopei Liubomyr Ropyak

The object of the research is the information interaction processes between the components of a simulation model of a PID controller based on Digital Twin technology. The problem addressed lies in the need to extend the functionality of various models when they are integrated into real control systems. The aim of the study is to develop a simulation model of a PID controller for electric-drive frequency-based control systems using Digital Twin technology. A concept for constructing a simulation model using unified hardware–software tools from Simatic S7 and Digital Twin technology is proposed. In this approach, virtual components of the simulation model are configured, parameterized, and programmed within the same engineering environment as the real ones. Projects developed based on simulation results of PID controllers provide the foundation for their implementation on real Simatic S7 hardware. The simulation model provides for integration and interaction of fully virtual components, including PLC, frequency converter, electric drive, SCADA, and communication environment. Procedures for parameterizing monitoring tools and for the automatic tuning of PID controller parameters according to the chosen strategy were implemented, which enabled a clear graphical evaluation of transient processes under different operating modes of the simulation model. The response of the PID controller to periodic and random disturbance signals within up to100% of the control range was tested.

Automation doi: 10.3390/automation7030073

Authors: Da An Ng Kok Why Fangfang Chua

In the original publication [...]

Automation doi: 10.3390/automation7030072

Authors: Carlos Villafuerte Melissa Moncayo William Oñate

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution.

Automation doi: 10.3390/automation7030071

Authors: Kanibek Sansyzbay Yelena Bakhtiyarova Yesbol Turgambay Laura Tasbolatova Aigerim Kismanova Akmaral Zhumagul

The article addresses the challenges of modernizing Kazakhstan’s railway infrastructure under conditions of technological dependence on foreign automation systems and obsolete relay-based equipment. These factors pose significant risks to economic and information security and limit the throughput capacity of level crossings. A digital system, KZ-DALCS-AI, is proposed, based on a multi-level safety architecture and the integration of artificial intelligence into monitoring and control processes. A key component is an obstacle detection and classification algorithm that considers object types (vehicles, humans and animals, foreign objects, and environmental factors) and enables intelligent real-time decision making using the KZ-ODC-AI controller with data from video surveillance, microwave sensors, and inductive loops. The system architecture, operational logic, and level crossing control algorithm are developed, including optimization of closing time by minimizing the deviation between calculated and actual values. The results of the performed calculations confirm the effectiveness of the proposed notification algorithm, ensuring the required level of safety while reducing unnecessary delays for road traffic. The implementation of the system improves throughput, reduces operational costs, enhances reliability, and minimizes the impact of the human factor.

Automation doi: 10.3390/automation7030070

Authors: Erbet Almeida Costa Odilon Santana Luiz de Abreu Galdir Reges Tiago de Oliveira Silva Carine Menezes Rebello Marcio Fontana Marcos Pellegrini Ribeiro Idelfonso B. R. Nogueira Leizer Schnitman

This study presents practical details and results from implementing model-based predictive controllers for artificial lift systems in oil production, which utilise electrical submersible pumps (ESPs) in legacy systems. The proposed methodology integrates the existing instrumentation architecture with new control systems and techniques. This work implements two control strategies: a standard MPC and an MPC with input targets. Both were executed in real time, encapsulated in a C++ executable, and deployed within Petrobras’s supervisory and control system. The results include an analysis of the instrumentation system, a discussion of operation under unmeasured disturbances and constraints, and a comparison of the controllers. The findings are extensive and indicate that both controllers stabilise the system, ensure constraint satisfaction, and appropriately compensate disturbances.

Automation doi: 10.3390/automation7030069

Authors: Mohd Ismail Yusof Fatin Nabilah Mohd Yasin Ayu Gareta Risangtuni Narendra Kurnia Putra Siti Hafshar Samseh Azavitra Zainal Mohd Aliff Afira Sani

This paper presents the application of a micro unmanned aerial vehicle (UAV) that acts as a pollination agent in a controlled environment simulating greenhouse conditions. The micro-UAV system was integrated with a convolutional neural network (CNN) for autonomous flower detection and navigation. The custom Sequential CNN architecture was used on board to perform real-time binary classification, accurately distinguishing flowers from non-flower objects. The fusion of this deep learning-based detection with precise micro-UAV navigation enables efficient identification and approaches to target flowers within optimal operational distances. Experimental evaluations revealed that the micro-UAV’s onboard camera, combined with CNN processing, outperformed standard webcams in terms of detection speed and accuracy, demonstrating the benefits of specialized hardware. Within the experiment, the micro-UAV was pre-programmed to follow a ‘cross’-shaped flight pattern. Experimental results show that the proposed system successfully detects multiple flowers autonomously between distances of 30.5 cm and 91.5 cm within 149.1 s. Overall, this study validated the integration of neural network capabilities with micro-UAV navigation. These findings are crucial for highlighting the potential of neural network-enabled micro-UAVs as effective pollinators in enclosed agricultural environments and for addressing the challenges faced by natural pollinators in greenhouses.

Automation doi: 10.3390/automation7030068

Authors: Oscar A. Tobar-Rosero Ivar F. Gomez-Pedraza John E. Candelo-Becerra Juan D. Grajales-Bustamante Fredy E. Hoyos

The increasing digitalization of electrical substations, enabled by IEC 61850-based architectures, has improved operational efficiency while expanding the cyber attack surface. This paper introduces a standards-aligned cybersecurity risk mitigation model specifically designed for digital substations and mapped to representative attack scenarios. The model integrates preventive, detective, and application-level controls derived from NIST SP 800-82r3, IEC 62443, and ISO/IEC 27019, and is validated in a laboratory process-bus environment. A baseline risk assessment identified four high-risk scenarios in the studied digital substation architecture. For validation, a selected subset of controls was experimentally evaluated against two representative attack vectors, namely false data injection (FDI) on GOOSE messages and denial-of-service (DoS) against PTP synchronization. For the remaining scenarios, the post-mitigation effects were reassessed analytically based on control coverage, architectural exposure, and standards-aligned cybersecurity reasoning. The experimental validation demonstrated that both empirically tested high-risk scenarios (FDI on GOOSE and DoS on PTP) were effectively mitigated, reducing their residual risk to moderate and low levels, respectively. For the remaining two scenarios, a post-mitigation analytical reassessment based on control coverage and architectural exposure suggested a consistent risk reduction trend, although without direct experimental confirmation. Under this combined empirical–analytical assessment, the number of high-risk scenarios decreased from four to one, corresponding to a 50% experimentally validated reduction in high-risk exposure, complemented by an analytical reassessment of the remaining scenarios. These results provide quantitative evidence about the effectiveness of the model, even with partial implementation. The scientific contribution of this study lies in integrating multistandard cybersecurity requirements into an operational mitigation model tailored to IEC 61850 substations, combined with experimental risk quantification in a realistic process-bus testbed. The proposed model offers practical guidance for utilities and establishes a scalable foundation for advancing cybersecurity in critical power infrastructure.

Automation doi: 10.3390/automation7030067

Authors: Janak Nambiar Samson Yu Ian Lilley Hieu Trinh

This study presents a techno-economic analysis of deploying distributed energy resources (DERs), specifically photovoltaic (PV), battery energy storage systems (BESSs) and electric vehicles (EVs), in apartment buildings configured as Virtual Power Plants (VPPs). Utilizing cooperative game theory, the research models strategic collaboration between apartment residents (demand side) and utility operators (plant side) to maximize energy efficiency and economic returns. The VPP structure is analyzed over a 15-year life cycle, incorporating net present value (NPV), payback period (PBP), and government subsidy impacts. A cooperative game framework is applied using the Shapley value to ensure fair profit allocation based on each party’s contribution. Results indicate improved self-sufficiency, peak load reduction, and mutual financial benefits. Scenario analyses show that government subsidies to the plant side significantly increase the likelihood of successful cooperation, while declining DER costs enhance the VPP’s economic viability. The findings demonstrate that apartments configured as VPPs achieve strong economic viability (39% ROI, 10.5-year payback) and operational performance (70% self-sufficiency, 40% peak reduction) when grid arbitrage is enabled and moderate government subsidies (35% PV, 45% BESS) are provided. This research provides a replicable model for urban energy planning and policy development, promoting sustainable energy transitions through shared DER infrastructure and cooperative stakeholder engagement.

Automation doi: 10.3390/automation7030066

Authors: Yow Onn Tang Muhammad I. N. Ma’arof Girma T. Chala

Computer numerical control (CNC) machining process simulation is increasingly central to intelligent manufacturing, yet its deployment in brownfield environments remains constrained by legacy controllers, heterogeneous data semantics, limited computational resources, and rising cybersecurity requirements. While digital twins (DTs), artificial intelligence (AI), and multi-physics simulation have matured conceptually, practical adoption, particularly among small and medium-sized enterprises (SMEs), continues to lag behind theoretical capability. This paper presents a PRISMA-guided systematic review of peer-reviewed literature, standards, and industrial reports published between 2019 and 2025, focusing on CNC machining simulation, digital twin architectures, interoperability standards, and intelligent optimisation under brownfield constraints. Rather than proposing new simulation algorithms, the review synthesises fragmented evidence into a deployable, standards-aligned integration perspective. The review consolidates prior work into a seven-layer architecture grounded in ISO 23247, explicitly separating sensing, communication, digital twin entities, analytics, and human–machine interaction. It derives practical decision rules for middleware selection, edge-cloud compute placement under latency constraints, and modelling strategy selection, ranging from mechanistic and finite-element methods to hybrid reduced-order and machine-learning surrogates. An SME-oriented implementation and validation roadmap links staged retrofitting to measurable operational indicators, including overall equipment effectiveness, first-pass yield, downtime, cycle time, and energy intensity.

Automation doi: 10.3390/automation7020065

Authors: Satyabrata Aich Sudipta Mohapatra Shrabani Nanda Taqdees Khan Ayushi Bharti Hajra Sultana Umashankari Kalaiarsan Chea Senghuy Okpete Uchenna Esther Ada Proloy Kumar Mondal Yong-Ki Lee

Accurate detection and severity estimation of corrosion on metallic surfaces is essential for maintaining material integrity and ensuring operational safety in industrial systems. To address limitations in manual inspection methods, this study presents a two-stage deep learning pipeline tailored for high-resolution scanning electron microscopy images. The framework combines instance-level corrosion segmentation using the YOLOv8-seg architecture with subsequent severity classification performed by EfficientNet-B0 and ResNet18. In the segmentation stage, models are trained using both manually annotated and automatically generated binary masks, enabling robust instance mask prediction through prototype-based mask decoding. The classification stage assesses the severity of corrosion by analyzing localized regions based on morphological features, leveraging convolutional neural networks optimized for binary output. The experimental results demonstrate strong performance: the segmentation model trained on manual annotations achieves a Mean Intersection over Union (mIoU) of 89.91, a mask mAP@50 of 98.6, and an ROC-AUC of 94.69. For severity classification, EfficientNet-B0 achieves an accuracy of 93.75% and an F1-score of 93.29, outperforming ResNet18. The proposed framework connects advanced SEM with state-of-the-art machine learning. It provides a scalable, annotation-efficient way to use intelligent and automated corrosion characterization in materials science and industrial applications.

Automation doi: 10.3390/automation7020064

Authors: Stelian-Emilian Oltean Mircea Dulau Adrian-Vasile Duka Tudor Covrig

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications.

Automation doi: 10.3390/automation7020063

Authors: Retz Mahima Devarapalli Raja Kumar Kontham

Industrial maintenance and predictive diagnostics constitute fundamental pillars of modern manufacturing that prevent equipment failures, minimize operational downtime, and optimize maintenance costs across diverse industrial environments. Vibration-based fault classification plays an important role in industrial operations, necessitating highly sophisticated diagnostic methodologies. This research addresses these industrial imperatives through a comprehensive investigation of novel hybrid deep learning architectures for vibration-based fault classification. This study introduces a strategic integration of Quadratic Neural Networks (QNNs), which demonstrate superior non-linear feature extraction capabilities on a vibration signal compared to traditional convolutional approaches. A systematic evaluation of seven sophisticated architectures establishes a clear performance hierarchy, with QuCNN-LSTM-Transformer emerging as the optimal model achieving 99.26% average accuracy. All proposed models demonstrate excellence, with test accuracies consistently surpassing 95% across all evaluated scenarios. The data analyzed is emprical utilizing sensor data collected from an experimental rig and shows exceptional performance consistency on CWRU and HUST datasets. This investigation establishes a new paradigm in intelligent diagnostics, offering functional guidance and definitive analysis of hybrid architectures that advance industrial fault classification applications.

Automation doi: 10.3390/automation7020062

Authors: Arthur Kniphoff da Cruz Ana Clara Hackenhaar Kellermann Ingridy Caroliny da Silva Jaine Mercia Fernandes de Oliveira Marcia Elena Jochims Kniphoff da Cruz Lorenz Däubler

To develop secure, fast, and interoperable smart substations, it is vital to understand the current situation and potential future directions of the technologies involved. This study presents the evolution and state of the art of the Generic Object Oriented Substation Event (GOOSE) communication protocol, defined by the International Electrotechnical Commission (IEC) 61850 standard. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. This included journal articles published from 2004 to 2025 and conference papers from 2020 to 2025, written in English within Engineering. Only studies primarily focusing on GOOSE, citing it at least ten times, and indexed in the Scopus, IEEE Xplore, and Web of Science databases were included. The quantitative analysis used SciMAT software, complemented by a qualitative analysis. Due to the bibliometric and thematic nature of this review, potential biases were considered at the review level rather than by applying a formal study-level risk-of-bias tool. The final analysis comprised 82 journal articles and 84 conference papers. The results offer a comprehensive mapping of GOOSE research evolution, identify nine main challenges and limitations from the last 22 years, and highlight current research directions. The literature reveals methodological heterogeneity, a predominance of simulation-based approaches, and limited large-scale empirical validation.

Automation doi: 10.3390/automation7020061

Authors: Lucero Ortiz-Aguilar Marcela Palacios-Ortega Martin Carpio Julio Funes-Tapia

The rapid integration of electric vehicles (EVs) into transportation systems and power grids has significantly increased the complexity of optimization challenges related to routing, charging coordination, scheduling, and energy management. Despite significant research growth, the field remains conceptually fragmented, lacking a unified framework to systematically organize Electric Vehicle Optimization Problems (EVOPs). To address this gap, this study presents a systematic review of 144 peer-reviewed articles published between 2011 and January 2025 and proposes a structured EVOP taxonomy based on problem characteristics and dominant decision variables. The analysis examines mathematical formulations, solution methodologies, and emerging research trends. The results indicate the predominance of metaheuristic methods, while exact techniques are mainly limited to small-scale problems. Additionally, there is a growing trend toward multi-objective and stochastic models that incorporate uncertainty and dynamic decision-making environments. However, challenges remain regarding large-scale validation, standardized benchmarking, and integrated multi-domain modeling. The proposed taxonomy provides a coherent framework that facilitates comparison across optimization domains and supports the development of scalable and intelligent EV management systems.

Automation doi: 10.3390/automation7020060

Authors: S. Ali Mousavi Ali Masoudian Mohammad Hassan Khooban

This paper presents an adaptive talkative power (TP) framework that enables simultaneous high-efficiency power transfer and reliable data communication under time-varying load conditions. A high-frequency TP-based bidirectional boost converter employing a SiC-based zero voltage switching–quasi square wave (ZVS-QSW) topology is proposed, incorporating closed-loop online efficiency optimization. Data transmission is realized through adaptive switching-frequency modulation at the transmitter, allowing information encoding while preserving optimal power transfer efficiency. To support reliable data detection under unknown and non-constant load conditions, an adaptive receiver architecture is developed that extracts information from output voltage ripple variations induced by frequency modulation. Owing to the nonlinear and complex nature of the ripple characteristics, a supervised machine-learning-based classification approach is employed for data detection, eliminating the need for prior knowledge of converter parameters and overcoming the limitations of conventional maximum-likelihood detection methods. The proposed system is validated through real-time simulations using a dSPACE MicroLabBox system in conjunction with MATLAB/Simulink R2025b. Simulation results demonstrate power transfer efficiencies approaching 98% while enabling reliable and efficient data transmission across a wide range of operating conditions, including varying conversion ratios and dynamic load variations, thereby confirming the effectiveness and robustness of the proposed TP-based power and data transmission scheme.

Automation doi: 10.3390/automation7020059

Authors: Yong Xu Jin Liu Hongtao Yan An Wang Haihang Xu Yue Ma Tian Yao

As the “visual perception hub” of unmanned aerial vehicles (UAVs), electro-optical (EO) pods play an increasingly critical role in tasks such as intelligence gathering, situational awareness, target tracking, and localization. With the expanding scope and depth of UAV applications, higher demands are placed on the precision and adaptability of installation error calibration techniques for EO pods. Current mainstream calibration methods typically require specialized procedures under constrained conditions, while few approaches integrate existing UAV system capabilities and mission requirements, which leads to cumbersome, time-consuming processes and suboptimal alignment between calibration outcomes and task objectives. This paper proposes an online calibration method for UAV EO pod installation errors based on target tracking, which can rapidly compute the optimal closed-form solution for installation errors by leveraging UAV tracking missions. First, an observation equation for pod installation errors is established using tracking results. Second, multi-temporal observations are combined to model the calibration problem as an optimal rotation matrix estimation task, and then the optimal closed-form solution for installation errors is derived. Concurrently, a statistics-based approximate calibration method is introduced specifically for tracking missions. Furthermore, an online calibration system compatible with diverse UAV platforms is designed, along with different rapid calibration schemes for emergency response scenarios, fully incorporating existing system capabilities and mission needs. Finally, a fixed-wing UAV experimental platform is developed, with calibration tests conducted under various flight regimes. Experimental results validate the feasibility and robustness of the proposed methodology.

Automation doi: 10.3390/automation7020058

Authors: Sadia Sadiq Muhammad Umer Sohail Muhammad Wasim Farooq Kifayat Ullah Zeashan Khan

Accurate prediction of store trajectories at the point of release from an unmanned/manned aircraft is an essential requirement for safety and precision. Captive Trajectory System (CTS) is a well-known feature of wind-tunnel testing to simulate the dynamics of store separation. To accurately replicate real-world aerodynamic conditions based on measured forces and moments, it utilizes a six-degree-of-freedom (6-DOF) robotic arm controlled by a closed-loop control system that solves the store’s equations of motion. In this study, a wing–pylon–store configuration is used as a sample case, and published experimental trajectories are used as input. A 6-DOF robotic arm named ROBO-S is designed to follow these trajectories in a CTS setup. The kinematic analysis of ROBO-S is performed in this study. The Denavit–Hartenberg (DH) method is used for the calculation of forward kinematics, whereas geometric techniques are used for inverse kinematics calculations. A simulation of kinematic analysis is performed in MATLAB R2021a. The mechanical design of ROBO-S is carried out in PTC CREO 9.0. MATLAB simulations confirm that the robotic arm can follow the trajectory obtained from published experimental results. To demonstrate the feasibility of the design, the robotic arm is fabricated using 3D printing. The results demonstrate the potential of the developed system in accurately following trajectories for wind-tunnel testing applications.

Automation doi: 10.3390/automation7020057

Authors: Ivan Bergier Jayme Garcia Arnal Barbedo Édson Luis Bolfe Debora Drucker Filipi Miranda Soares

New regulations, such as the EU Deforestation-Free Regulation (EUDR), make verifiable agricultural data (AgData) essential for global trade. However, its value is compromised by a widespread “AgData Paradox”, characterized by distrust and fragmentation. To address this problem, we present AgriTrust, a federated semantic governance framework that automates and governs data sharing. Its key methodological innovation lies in the deep integration of a multi-sectorial governance model with a semantic digital layer, implemented through the AgriTrust Ontology (an OWL ontology for tokenization and traceability) and a multi-vendor, blockchain-agnostic architecture that avoids single-vendor dependence. We demonstrate the framework’s feasibility through simulated case studies in three critical Brazilian supply chains: coffee (EUDR compliance), soybean (mass balance), and beef (animal traceability). Using a semantic reasoning pipeline on a proof-of-concept federated knowledge graph of 2010 triples, we show how AgriTrust enables verifiable provenance representation, automated compliance checking via executable data contracts, and cross-platform asset management. The results provide initial evidence that AgriTrust offers a conceptually coherent blueprint for agricultural data sharing, though operational deployment, scalability testing, and performance validation under real-world conditions remain as future work.

Automation doi: 10.3390/automation7020056

Authors: Aliyu Tanko Ali Andreas Schuldei Martin Sachenbacher Martin Leucker

We present a study to assess the feasibility and implications of replacing internal combustion engine vehicles (ICEVs) with battery-powered electric vehicles (EVs) in a car-sharing fleet. For the analysis, we used operational data from a local car-sharing company, which encompasses various aspects such as trip distance, start and duration, vehicle type, and pickup and return locations. To evaluate the impact of transitioning the entire fleet to EVs, we used EV and charger models to simulate the battery-powered trips and the necessary post-trip recharging. Both could affect the service quality of car-sharing services, as the requested trip distance might not be covered by an electric vehicle due to range or charging time limitations. Specifically, in our simulation-based analysis, we identified chains of consecutive bookings as a critical factor for car-sharing electrification. Furthermore, to assess the potential impact of electrification on the energy grid, we used data about the local grid load and its composition to relate it to the predicted vehicle charging times. This is an extended version of our previous paper, incorporating an additional dataset.

Automation doi: 10.3390/automation7020055

Authors: Khairul Muzaka Liyanage Chandratilak De Silva Wahyu Caesarendra

This research presents a machine learning-based vibration signal acquired from aluminum grinding experiment for potential application in smart and intelligent manufacturing. The study addresses the challenges of traditional surface finishing quality inspection by integrating vibration sensing and support vector machine (SVM). A robot manipulator lab grinding experiment consist of a four-axis DOBOT Magician with a handheld cylindrical grinding tool attached on the end-effector of the DOBOT Magician. This customized lab grinding experiment was designed to perform consistent surface finishing experiment for different aluminum work coupon and time duration. Triaxial accelerometer was used to collect the vibration signal and to investigate the most relevant vibration signal direction (x, y, and z) to the surface quality prediction of the aluminum work coupon. The vibration signal was acquired via LabVIEW and NI data acquisition (DAQ) system. The vibration features were extracted and analyzed using Python programming in Google Colab. The SVM algorithm in Python (3.11 and 3.12) is used to classify surface roughness quality into coarse, medium, and fine categories based on the extracted vibration features. Vibration feature parameters such as root mean square (RMS), Peak to RMS, Skewness, and Kurtosis were also investigated to determined which feature pairs are most critical for effective surface roughness monitoring and prediction using SVM classification. The classification model achieved high accuracy across all three vibration axes (x, y, and z), with the z-axis yielding the most consistent results. The proposed system has potential applications in real-time surface quality prediction within smart manufacturing practices aligned with Industry 4.0 principles.

Automation doi: 10.3390/automation7020054

Authors: Sunnatillo T. Boltayev Bobomurod B. Rakhmonov Obidjon O. Muhiddinov Sohibjamol I. Valiyev Muxammadaziz Y. Xokimjonov Eldorbek G. Khujamkulov Sherzod F. Kholboev Egamberdi Sh Joniqulov

This paper presents an integrated mathematical model to improve the safety and operational efficiency of train traffic in centralised railway dispatching systems. The proposed approach combines the alternative graph model with a Mealy automaton to synchronously address route planning, delay minimisation, and strict compliance with safety requirements. Formal control theory based on finite-state automata is employed to describe routing logic and signal control through state transitions, while the alternative graph model represents scheduling constraints and resource conflicts. To enhance real-time adaptability, a tabu search algorithm is implemented for train schedule optimisation, enabling dynamic rescheduling under changing operational conditions. The mathematical formulation incorporates blocking time parameters, a system of discrete constraints, and automaton-based safety conditions governing train movements and route authorisation. The integrated model explicitly formalises the processes of block section occupation and release, ensuring consistency between control logic and scheduling decisions. Practical testing and computational experiments demonstrate that the proposed approach effectively reduces train delays, improves the reliability of dispatch control, and increases system resilience to dynamic disturbances. The results confirm that the developed model can be implemented within existing centralised dispatching infrastructures without requiring a complete system overhaul. Overall, the proposed framework expands the functional capabilities of centralised dispatch systems by enabling efficient schedule generation, minimising the propagation of delays, and ensuring reliable command exchange between central control posts and field-level railway infrastructure.

Automation doi: 10.3390/automation7020053

Authors: Salma Jnayah Adel Khedher

This paper presents an efficient stand-alone photovoltaic water pumping system (PVWPS) intended for agricultural irrigation applications, operating without energy storage. The system employs a three-phase induction motor supplied by a three-level neutral point clamped (NPC) inverter. The proposed control strategy integrates the advantages of two distinct controllers to enhance both energy extraction and drive performance. On the photovoltaic side, a fuzzy logic-based maximum power point tracking (MPPT) algorithm is implemented to ensure continuous operation at the global maximum power point under rapidly varying irradiance conditions. On the motor drive side, a direct torque control (DTC) scheme is combined with the multilevel NPC inverter to regulate electromagnetic torque and stator flux. The use of a multilevel inverter significantly mitigates the inherent drawbacks of conventional DTC, notably torque and flux ripples, as well as stator current harmonic distortion. The overall control architecture maximizes power transfer from the photovoltaic generator to the pumping system, resulting in improved dynamic response and energy efficiency. The proposed system is validated through detailed MATLAB/Simulink simulations under abrupt irradiance variations and a realistic daily solar profile corresponding to August conditions in Kairouan, Tunisia. Simulation results demonstrate substantial performance improvements, including an 88% reduction in torque ripples, a 50% decrease in flux ripple, a 77.9% reduction in stator current THD, and a 33.3% enhancement in speed transient response compared to conventional DTC-based systems.

Automation doi: 10.3390/automation7020052

Authors: Emma N. Zavacky Ahlad Neti Cheng-Shiu Chung Alicia M. Koontz

Upper extremity (UE) pain and pathology are prevalent among manual wheelchair users (MWUs) due to repetitive loading demands, highlighting the need for tools to identify high-risk tasks and inform injury prevention. This study investigated the feasibility of classifying activity intensity for wheelchair-related tasks using wearable sensors and supervised machine learning. Twenty-four MWUs with chronic spinal cord injury completed a standardized mobility course and simulated activities of daily living while UE electromyography (EMG) and inertial measurement unit (IMU) data were collected. Signals segmented into 3, 5, and 10 s windows, and time- and frequency-domain features were extracted and labeled as low, moderate, or high intensity. Multiple classification algorithms were evaluated using subject-dependent and subject-independent cross-validation, and dimensionality reduction was explored to assess class separability. Subject-dependent analyses demonstrated performance above chance but below 75% accuracy, with decision tree models demonstrating superior performance, particularly when trained on data segmented into 5 s windows. IMU features outperformed EMG features, but combining signal types enhanced performance. Subject-independent analyses revealed similar overall accuracy across signal types, but decreased high-intensity classification for EMG data, indicating subject dependency. Findings support the potential of wearable sensor-based machine learning with population-specific findings for activity intensity classification in MWUs, while highlighting challenges related to inter-subject variability for injury risk prediction.

Automation doi: 10.3390/automation7020051

Authors: Ali Mazinani Mostafa Norouzi Amin Talaeizadeh Aria Alasty Mahmoud Saadat Foumani Amin Kolahdooz

Accurate real-time detection, geolocation, and counting of palm trees are essential for plantation management, yield estimation, and resource allocation in precision agriculture. Traditional approaches such as manual surveys or offline image processing are labor-intensive and unsuitable for large-scale applications. This study introduces a fully onboard real-time framework that integrates Unmanned Aerial Vehivle (UAV) imagery, the YOLOv12 deep learning model, and a camera projection technique to detect, geolocate, and count palm trees directly during flight. The lightweight YOLOv12n variant, deployed on an NVIDIA Jetson Nano edge device, achieved a detection precision of 92.4%, an average geolocation error of 2.14 m, and a counting error of only 0.2% across 915 trees. Unlike many existing methods that rely on offline processing or offboard computation, the proposed system performs all computations in real time, enabling immediate decision-making for tasks such as plantation density analysis, replanting planning, and yield forecasting. Experimental results demonstrate that the proposed approach provides a scalable, cost-effective, and autonomous solution for modern precision agriculture.

Automation doi: 10.3390/automation7020050

Authors: Sujatha Banka D. V. Ashok Kumar

In the era of renewable dominated grids, integration of dynamic loads such as EV charging stations have increased the operational challenges in multifolds, particularly in DC microgrids (DC MGs). Traditional battery-dominated grid energy management strategies (EMSs) are often not capable of handling fast transients due to the limitations of battery electrochemistry. To overcome this limitation, a hierarchical hybrid energy management strategy is proposed that uses the combination of data-driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and a neural network (NN) implemented in the central controller of a 4-bus ringmain DC MG. An efficient decoupling of fast and slow storage dynamics is performed, where the supercapacitor (SC) is optimized using the NN and the battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for real-time implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on a real-time hardware setup. Robustness of the control scheme is verified with various case studies, such as renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of the SC in both transient and heavy load scenarios are observed. LabVIEW interfacing is used for MODBUS-based interaction with PV emulators and DC-DC converters.

Automation doi: 10.3390/automation7020049

Authors: Ricardo Rojas-Galván Josué A. Romero-Moreno Roberto V. Carrillo-Serrano José R. García-Martínez Trinidad Martínez-Sánchez Mario Trejo-Perea José G. Ríos-Moreno Juvenal Rodríguez-Reséndiz

Two-wheeled self-balancing robots (TWSBRs) are difficult to control because they are nonlinear, unstable, and underactuated, particularly when balance, velocity regulation, and line tracking must be achieved simultaneously. This paper proposes a hybrid parallel control architecture for a line-following TWSBR operating on straight segments, 90∘ curves, and a 15∘ slope. Balance stabilization is handled by a classical PD loop, while traslational velocity is regulated by an adaptive fuzzy PI controller, and line following is performed with an adaptive fuzzy PD controller. The fuzzy modules adjust the effective gains based on tracking errors, thereby improving robustness to disturbances, sensor noise, and changes in operating conditions. The complete strategy is implemented on a low-cost PIC18F4550 microcontroller. Experiments show that the fuzzy line-following controller reduces the orientation tracking error compared with a conventional controller. At 0.10ms, RMSE decreases from 0.042rad to 0.038rad, and at 0.175ms, it decreases from 0.083rad to 0.066rad. The fuzzy approach also improves IAE (1.317rads to 1.185rads) and ISE (0.242rad2s to 0.153rad2s) at 0.175ms, while maintaining similar maximum error (0.299rad to 0.261rad). Overall, the proposed hybrid scheme achieves better adaptability without retuning. These results support real-time deployment on resource-limited platforms.

Automation doi: 10.3390/automation7020048

Authors: Zhe Zhang Chang Lin Bing Fang

Reliable ship detection in complex maritime optical imagery is a fundamental requirement for intelligent maritime monitoring and maritime automation systems. However, severe image degradation, large-scale variations, and background clutter often lead to feature ambiguity and unstable detection performance in real-world maritime environments. To address these challenges, this paper proposes a lightweight one-stage ship detection framework designed for robust real-time perception under degraded maritime sensing conditions. The proposed method incorporates an Adaptive Expert Selection Attention (AESA) mechanism to perform adaptive feature selection and background suppression under visually degraded conditions, together with a Geometry-Aware MultiScale Fusion (GAMF) module that enables orientation-aware aggregation of contextual information for elongated ship targets near complex sea–sky boundaries. In addition, a geometry-aware bounding box regression refinement is introduced to improve localization consistency in image space. Extensive experiments conducted on a unified real-world maritime benchmark demonstrate that the proposed framework consistently outperforms the baseline YOLO11n model by approximately 2–5 percentage points in terms of mAP@0.5 and mAP@0.5:0.95, while maintaining moderate computational complexity and real-time inference capability. These results indicate that the proposed method provides a practical and deployment-oriented perception solution for maritime automation applications, including onboard electro-optical sensing and coastal surveillance.

Automation doi: 10.3390/automation7020047

Authors: Infanta Mary Priya Prabhu Sethuramalingam Hruday Divakaran Dennis Abraham Archit Srivastava Ayush K. Choudhary Allen Mathews Amish Roopesh Sidhant Sairam Mohan Naman Vedh K. Sathyan

The hyperloop transportation system is a promising ultra-high-speed mobility solution operating in a reduced-pressure environment, where pod performance is governed by the coupled behaviour of structural integrity, aerodynamics, and electromagnetic propulsion. This paper presents the design, numerical analysis, and performance evaluation of a lightweight hyperloop pod equipped with a linear induction motor (LIM)-based propulsion and electromagnetic stabilisation system. The pod chassis was fabricated using Carbon Fibre-Reinforced Polymer (CFRP) and Aluminium 6061-T6, achieving a significant weight reduction while maintaining structural safety. Finite Element Analysis reveals a maximum von Mises stress of 82 MPa, which is well below the material yield strength, and a maximum deformation of 0.64 mm under worst-case loading conditions. Modal analysis indicates the first natural frequency at 47.6 Hz, ensuring sufficient separation from operational excitation frequencies. Computational Fluid Dynamics analysis conducted inside a rectangular tube shows a drag coefficient reduction of approximately 18% compared to a baseline blunt design, with stable velocity distribution and no flow choking at operating speeds. The optimised nose geometry enables rapid acceleration, achieving 25 km/h within 1.1 s in prototype testing. The LIM analysis demonstrates a peak thrust of 1.85 kN at an optimal slip range of 6–8%, with operating currents between 35 and 55A and power consumption of 18–25 kW. Thermal analysis confirms a maximum stator temperature of 78 °C, remaining within safe operating limits. The integrated numerical and experimental results confirm the feasibility, efficiency, and stability of the proposed hyperloop pod design.

Automation doi: 10.3390/automation7020046

Authors: Siwakorn Sukprasertchai Yodchai Tiaple

This paper presents a model-free Best Efficiency Point (BEP) tracking method for centrifugal pumps without head measurement or manufacturer-provided characteristic curves. The proposed approach combines a discrete finite-difference extremum-seeking control (ESC) scheme with an efficiency approximation proxy derived from measurable variables—namely, flow rate and electrical power. Under constant head conditions, the proxy function is analytically shown to be proportional to the true pump efficiency, enabling real-time BEP localization using only motor feedback signals. The ESC algorithm employs a sign-based gradient rule with adaptive step-size reduction to achieve rapid and stable convergence without mathematical models. A Python-based simulation using a Schneider SUB 15-0.5cv pump demonstrates that the method can track the BEP with negligible steady-state error (less than 0.1% efficiency deviation). The proposed framework offers a cost-effective solution for efficient optimization for mobile pumping applications in large water resources where installing head sensors is impractical.

Automation doi: 10.3390/automation7020045

Authors: Attila Kovács Judit Kovácsné Molnár Károly Jármai

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T2, Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism.

Automation doi: 10.3390/automation7020044

Authors: Taoufik Belkebir Hicham Belkebir Anass Mansouri

Local path tracking is a critical challenge for autonomous vehicles, requiring precise trajectory following under nonlinear dynamics, strict constraints, and real-time execution. While Nonlinear Model Predictive Control (NMPC) has emerged as a leading approach, many existing methods decouple velocity planning from tracking, lack formal stability guarantees, or do not demonstrate feasibility on embedded platforms. We present a unified NMPC framework that integrates curvature-aware velocity adaptation directly into the cost function. The controller makes use of cubic spline paths, recursive feasibility constraints, and Lyapunov-based terminal costs to ensure stability. The nonlinear optimization problem is implemented in CasADi and solved using IPOPT, with warm-starting and efficient discretization techniques enabling real-time performance. Our approach has been validated in the CARLA simulator across a variety of urban scenarios, including straight roads, intersections, and roundabouts. The controller achieves a mean cross-track error of 0.10 m on straight roads, 0.44 m on roundabouts, and 1.36 m on tight intersections, while maintaining smooth control inputs and bounded actuator effort. A curvature-aware cost term yields a 14.4% reduction in lateral tracking error compared to the curvature-unaware baseline. Benchmarking results indicate that the Raspberry Pi 5 outperforms the NVIDIA Xavier AGX by 1.5–1.6×, achieving mean execution times of 38–45 ms across all scenarios. This demonstrates that advanced NMPC can run in real time on low-cost consumer hardware ($80 vs. $700). Systematic ablation studies reveal the critical role of state weighting (Q) and input regularization (R): removing Q degrades tracking by 10% and destabilizes control effort (+54% acceleration, +477% steering), while omitting R induces oscillatory behavior with +907% acceleration effort. Euler integration provides no computational benefit (+8% solver time) while degrading accuracy by 25%, confirming RK4 as strictly superior. Sensitivity analysis via Latin Hypercube Sampling identifies the prediction horizon (N) and discretization timestep (Δt) as dominant parameters. Per-scenario Pareto analysis yields a balanced operating point (N=15, Δt=0.055 s) used for all primary evaluations, while a global sweep identifies a robust alternative (N=12, Δt=0.086 s) suitable for general deployment tuning. This framework bridges the gap between spline-based local planning and stability-guaranteed NMPC, offering a simulation-validated, real-time solution for embedded autonomous driving research. Future work will focus on hardware-in-the-loop and full-vehicle deployment, integration with high-level decision-making, and learning-enhanced MPC.

Automation doi: 10.3390/automation7020043

Authors: Xiuzhen Wu Yahui Qi Liang Qin Shi Yan Jianxiu Zhang

Due to their advantages of being low-cost, lightweight and flexible, and having wide shooting coverage, UAVs have played an important role in situational awareness in the fields of disaster prevention and mitigation, urban planning and management, etc. In these applications, UAV aerial photography is limited by the field of view, and high-definition panoramic images of the complete target area cannot be obtained. Image mosaic technology is essential, but an image mosaic using only a single UAV cannot meet the high real-time requirements for situational awareness. In response to the above problems, this paper proposes a multi-UAV fast aerial image mosaic method based on key frames. First, the multi-UAV area coverage flight strategy is determined according to the size of the task area and the UAV flight parameters; then, the field of view of the pod, the flight speed, and the flight altitude are used to determine the key frame extraction time period during the UAV aerial photography process. The image matching-rate calculation method is designed and the key frames are extracted during the extraction time period, and the key frames are returned to the ground visual puzzle system; in the ground visual puzzle system, the improved Laplacian pyramid method is used to quickly fuse and stitch the key frames extracted by each UAV to obtain a panoramic stitched map. The experiment shows that the method can quickly obtain high-precision real-scene map information of the task area. Compared with the single-UAV method and the multi-UAV full video stream-splicing method, this method greatly reduces the consumption of computing power and the requirements of communication bandwidth and improves the efficiency and real-time performance of panoramic map acquisition.

Automation doi: 10.3390/automation7020042

Authors: Narendra Kumar Kolla Pandu Ranga Vundavilli

Simultaneous Localization and Mapping (SLAM) is essential for autonomous movement in intelligent robotic systems. Traditional SLAM using a single sensor, such as an Inertial Measurement Unit (IMU), faces challenges including noise and drift. This paper introduces a novel Cartographer-based SLAM approach for DAMR trajectory generation in indoor environments to reduce drift errors and improve localization accuracy. This SLAM approach integrates multi-sensor data with extended Kalman filter (EKF) fusion from wheel odometry, an RGB-D camera (RTAB-Map), and an IMU for precise mapping with DAMR trajectory generation and is compared with the heading reference trajectory generated by robot pose estimation and frame transformation. This system is implemented in the Robot Operating System (ROS 2) for coordinated data acquisition, processing, and visualization. After experimental verification, the DAMR trajectories generated are closer to the reference trajectory and drift errors are tuned. The experimental results revealed that the DAMR trajectory with multi-sensor data integration using the EKF effectively improved the positioning accuracy and robustness of the system. The proposed approach shows improved alignment with the reference trajectory, yielding a mean displacement error of 0.352% and an absolute trajectory error of 0.007 m, highlighting the effectiveness of the fusion approach for accurate indoor robot navigation.

Automation doi: 10.3390/automation7020041

Authors: Syed Salman Shah Abid Imran Saad-Ur-Rehman Arsalan Arif Khurram Khan Muhammad Arsalan Sajjad Manzoor Ghulam Jawad Sirewal

People affected by vision impairment experience significant challenges in mobility and daily life activities. In this paper, a smart assistive navigation system is proposed to address mobility challenges and to enhance the independence of visually impaired individuals. Three modules are integrated into the proposed system. The vision module detects obstacles and interactive objects such as doors, chairs, people, fire extinguishers, etc. The depth cam-based distance module provides the distance of detected objects and obstacles. The voice module provides auditory feedback to visually impaired individuals about the detected objects and obstacles that fall under the pre-defined threshold distance. Finally, the proposed system is optimized in terms of performance and user experience. Jetson Nano is used to reduce the cost of the overall system; however, it has compatibility issues with many of the latest object detection models. The YOLOv5n model is used considering compatibility for object detection, but it has low Mean Average Precision (mAP) and frame rate. To improve the performance of the vision module, various hyperparameters of YOLOv5n are fine-tuned along with transfer learning to enhance the mAP@50 from the original 0.457 to 0.845 and mAP@50-95 from 0.28 to 0.593. Tensor-RT optimization is employed to increase the frame rate to deploy the model in a real scenario. The real-time experimentation shows that the proposed system successfully alerts users to key objects, hazards, and obstacles which enables independent and confident navigation.

Automation doi: 10.3390/automation7020040

Authors: Azeddine Beloufa Souaad Tahraoui Abderrahmane Kacimi Hadje Allouache Jun-Jiat Tiang Abdelbasset Azzouz Mehdi Houari Zaid

The design of robust controllers for complex nonlinear systems remains a formidable challenge, particularly concerning the disparity between simulation performance and real-world implementation constraints. This research investigates the practical implementation of a backstepping controller integrated with a High-Gain Observer (HGO) on a Twin Rotor MIMO System (TRMS). While the control architecture exhibited stability and precise tracking in simulation, physical deployment initially failed due to sensitivity to measurement noise and the peaking phenomenon, resulting in a divergent response with a Yaw RMSE of 2.56 rad. Unlike conventional approaches that attempt to bridge the simulation-to-reality gap by optimizing the controller, we hypothesized that the critical bottleneck lay within the observer dynamics. To address this, a Radial Basis Function (RBF) Neural Network was employed to adaptively tune the observer gains in real time. Experimental results demonstrate that this adaptive mechanism successfully mitigated the effects of unmodeled dynamics and noise, reducing the Root Mean Square Error (RMSE) by over 85% in the pitch axis and 95% in the yaw axis. These findings substantiate that online adaptive observer tuning is a decisive strategy for ensuring the reliability of advanced nonlinear controllers on physical hardware.