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.

Automation doi: 10.3390/automation7020039

Authors: Amal Habibi Zied Hajaiej Mohamed Habibi

This paper presents a collaborative hazard-detection system that pairs a UAV running YOLOv9 for rapid aerial scanning with a UGV running Faster R-CNN for precise ground-level confirmation. The pipeline exploits complementary strengths, fast wide-area cueing from the air and high-precision verification on the ground, to reduce false alarms while maintaining responsiveness in complex environments. On the validation set, YOLOv9 reached mAP@0.5 = 0.969 with F1 = 0.95 at 41.7 FPS, enabling real-time scanning of large areas. Faster R-CNN attained mAP@0.5 = 0.979 with F1 = 0.95 at 1.72 FPS, providing reliable close-range confirmations where localization accuracy is critical. Together, these results show that the proposed UAV–UGV pipeline delivers a practical balance between rapid hazard identification and trustworthy validation, suitable for search and rescue, critical infrastructure monitoring, and operations in hazardous environments. Potential extensions include inference optimization on the ground platform, multi-sensor data fusion, and field trials to assess robustness under real-world conditions.

Automation doi: 10.3390/automation7020038

Authors: Guangkai Ma Jianwen Zhang Jiheng Jiang

In the manufacturing of high-reliability components, sinusoidal wobble laser welding has gained preference due to its excellent performance. However, surface defect inspection for such welds is challenged by large variations in defect scales, the coexistence of multiple defects, and scarce samples, which collectively limit existing detection methods. To address these issues, this paper proposes a lightweight detection framework that integrates a generative adversarial network with an improved YOLO architecture. First, a frequency-domain-enhanced StyleGAN2-AFMS model is constructed to effectively augment high-quality defect samples. Second, a YOLO11n-WAFE detector is designed, which incorporates an ADownECA downsampling module to enhance the capability of capturing subtle defects and an Edge-Aware Semantic–Detail Fusion module to improve discriminative robustness under multi-defect conditions. To validate the approach, an industrial-level Sinusoidal Wobble Laser Weld Defect Dataset is built. Experiments reveal that the proposed framework boosts mAP@0.5 to 94.2% (an 8% improvement over the baseline) and mAP@0.5:0.95 to 77.4%, with an F1-score of 89.5%, while maintaining lightweight (2.15 M parameters) and fast (656 FPS) characteristics. This study provides a high-precision and efficient solution for few-shot industrial defect inspection.

Automation doi: 10.3390/automation7020037

Authors: Alaq F. Hasan Firas Abdulrazzaq Raheem Amjad J. Humaidi

Artificial ventilation systems play a crucial role in the field of respiratory care, especially in intensive care units and surgical environments, where patients often require assisted breathing due to conditions such as acute respiratory distress syndrome (ARDS) or the effects of anesthesia. This study focuses on the development of an adaptive proportional–integral–derivative (PID) controller enhanced by a backpropagation neural network (BPNN) algorithm to accurately track airway pressure throughout the mechanical ventilation process. To achieve this, a MATLAB/Simulink model of a blower-driven patient hose (BDPH) ventilator system is constructed. Then, the performance efficiency of the artificial ventilation system is evaluated and analyzed based on the proposed control scheme under different operational scenarios. Furthermore, an analysis and comparison study of the performance of the adaptive PID controller based on the BPNN method and the classical PID controller has been conducted in terms of the robustness properties and transient behavior of the system. Simulation outcomes indicate that the adaptive PID controller showed faster convergence to the target airway pressure compared to the classical PID controller. This performance advantage arises from the controller’s ability to continuously adapt its gains to changes in operational conditions.

Automation doi: 10.3390/automation7020036

Authors: Shahab Ul Islam Giampaolo Ferraioli Ghassan Husnain Abdul Waheed Vito Pascazio

Tomato cultivation is a cornerstone of global agriculture, yet it faces significant challenges from a variety of diseases that can drastically reduce yield and quality. Traditional methods of disease detection, which rely on manual inspection, are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study presents an advanced, automated system for tomato disease detection and spray prescription using an enhanced YOLOv9 (You Only Look Once) model. By leveraging advanced deep learning techniques, the proposed system accurately identifies and detects nine tomato leaf diseases in real-time by making efficient, precise, and accurate decisions. This YOLOv9 model is modified for detecting tomato leaf diseases and optimized for getting higher accuracy and efficiency. The system automatically prescribes the spray based on detected disease, which helps in reducing pesticide use, along with the environmental impact. This system helps in maximizing crop health and yield. After testing the system on the test dataset and real-time images, the results demonstrate the system’s accuracy and efficiency, achieving a detection accuracy of 97% and spray prescription accuracy of 94%. Integrating a YOLOv9 with a spray prescription system provides a sustainable, cost-effective solution for managing tomato plant diseases. Implementing this system on edge devices paves the way for more extensive precision agriculture applications. By integrating advanced technology with real-world agricultural needs, this work makes a contribution and a global effort to ensure food security and ecological farming practices.

Automation doi: 10.3390/automation7010035

Authors: Omar Hanif Patrick Gruber Aldo Sorniotti Umberto Montanaro

Vehicle platooning is a cooperative driving scenario in which a set of consecutive, connected and autonomous vehicles travel at the same speed while controlling their inter-vehicular distance. Organising traffic in platoons of vehicles can mitigate issues in road transport by improving safety, energy efficiency, and road usage. Vehicle platooning scenarios are enabled by communication across the fleet, allowing the design of distributed controllers to impose cooperative vehicle motion. In contrast to initial control strategies tailored for specific network topologies, the last decade has witnessed a substantial increase in vehicle platooning control solutions that treat the cooperative platoon motion as the synchronisation of a network of dynamic systems, thereby enabling their use across a wider range of topologies. Despite numerous publications in recent years, the literature lacks a comprehensive survey of network synchronisation methods for vehicle platooning. To fill this gap, this paper aims to review network synchronisation strategies proposed for controlling the longitudinal motion of vehicle platoons over the period 2013–2025, with particular focus on contributions from 2018 onwards. The literature on network-synchronisation-based vehicle platooning methods is reviewed within a four-component framework. Then, the most widely used families of distributed consensus controllers are analysed, and the ways in which heterogeneity, nonlinearities, delays, packet drops, external disturbances, and cyber attacks are accounted for and mitigated are examined, along with different types of closed-loop stability. The review also surveys approaches from the literature for validating and assessing synchronisation algorithms in vehicle platoons, covering both experimental and simulation studies, as well as the related simulation platforms. The review paper concludes by presenting research trends and gaps, as well as potential future directions.

Automation doi: 10.3390/automation7010034

Authors: Diego Fernando Ramírez-Jiménez Claudia Milena González-Arbeláez P. A. Muñoz-Gutiérrez

In a globalized world where data play a critical role in system operation, process automation, and decision-making, the development of real-time control systems is essential, as it enables operators and supervisors to monitor the current status of a process based on its physical variables. Consequently, a wide range of software and hardware platforms is currently available for implementing real-time control systems, including Arduino, ESP32, and PIC microcontrollers. However, these platforms lack sufficiently robust hardware features for closed-loop control applications, as they were primarily designed for general-purpose use. To address the limitations of conventional embedded systems, this paper presents a novel approach for the implementation of digital controllers using Texas Instruments embedded systems applied to experimental plants designed with different control strategies. The proposed contribution focuses on the development of an experimental framework that integrates multi-platform programming, automatic code generation, and the use of dedicated real-time control modules, such as the Control Law Accelerator available in the LAUNCHXL-F28379D LaunchPad embedded system. The results highlight the capability of Texas Instruments microcontrollers to execute real-time control loops applied to different physical systems and operating under various control parameters. In conclusion, the findings demonstrate that Texas Instruments embedded systems equipped with advanced microcontroller architectures represent a promising alternative not only for scalable control applications but also for industrial-level control system development.

Automation doi: 10.3390/automation7010033

Authors: Salma Jnayah Zouhaira Ben Mahmoud Thouraya Guenenna Adel Khedher

Common-mode voltage (CMV) is a critical concern in motor drive applications employing multilevel inverters, as it can lead to significant issues such as high-frequency noise, electromagnetic interference, and motor bearing degradation. These effects can compromise the reliability, reduce the operational lifespan of electric machines, and introduce safety hazards. In this study, an enhanced Direct Torque Control (DTC) strategy incorporating Space Vector Modulation (SVM) is proposed to specifically address CMV-related challenges in induction motors (IM) driven by a three-level Neutral-Point-Clamped (NPC) inverter. The proposed DTC scheme utilizes a specialized modulation technique that effectively mitigates CMV while also minimizing current harmonic content, and torque and flux ripples with a constant switching frequency. The developed SVM algorithm simplifies the three-level space vector representation into six equivalent two-level diagrams, enabling more efficient control. The zero-voltage vector is synthesized virtually by combining two active vectors within a two-level hexagonal structure. The effectiveness of the proposed DTC approach is validated through both simulation and Hardware-In-the-Loop (HIL) testing. Compared to the conventional DTC method, the proposed solution demonstrates superior performance in CMV minimization and leakage current reduction. Notably, it limits the CMV amplitude to Vdc/6, a significant improvement over the Vdc/2 typically observed with the standard DTC approach.

Automation doi: 10.3390/automation7010032

Authors: Da An Ng Kok Why Fangfang Chua

Industrial surface defect detection is crucial for quality control in manufacturing, yet remains challenging due to the small scale, low contrast, and texture variability of defects. While YOLOv8n offers high inference speed and efficiency, its accuracy is limited by insufficient feature representation and inadequate data diversity. This paper proposes a detection framework integrating Channel–Spatial Modulation Attention (CASM) and Small-Scale Grid Texture Shuffling Augmentation (SG-TSA) into YOLOv8n to improve detection performance without sacrificing efficiency. CASM introduces a parallel channel–spatial attention structure with adaptive fusion to better capture fine-grained defect features, while SG-TSA increases sample diversity by introducing realistic texture perturbations within defect regions. Experiments on the NEU-DET dataset show that our method improves mAP@0.5:0.95 by 3.01% and mAP@0.5 by 2.84% over baseline YOLOv8n. These results highlight the importance of architecture-specific optimization for lightweight detectors in industrial scenarios.

Automation doi: 10.3390/automation7010031

Authors: Mouna Zerzeri Adel Khedher

This paper presents a robust sensorless control strategy for a dual-inverter doubly fed induction motor (DFIM) designed for high-performance electric vehicle (EV) traction systems. The proposed scheme eliminates the mechanical speed sensor by employing a sliding-mode observer (SMO) for real-time estimation of rotor speed and flux, ensuring accurate feedback under load disturbances and thereby enhancing reliability while reducing implementation cost. The DFIM is powered by two voltage-source inverters that independently control the stator and rotor windings through space vector pulse-width modulation (SVPWM). A power-sharing strategy optimally distributes the electromagnetic power between the two converters, ensuring smooth transitions between sub-synchronous and super-synchronous operating modes. Furthermore, a stator-flux-oriented vector control (SFOC) scheme incorporating a graphical torque optimization algorithm is developed to maximize torque while satisfying inverter and machine constraints across both base-speed and flux-weakening regions. The stability of the SMO-based estimation and closed-loop control is rigorously validated using Lyapunov theory. Comprehensive MATLAB R2024b/Simulink simulations conducted under the WLTC-Class 3 driving cycle confirm high accuracy and robustness, showing fast dynamic response, precise speed estimation, and smooth torque behavior across the full speed range. The results demonstrate that the SMO-based DFIM drive offers a cost-effective and reliable solution for next-generation EV traction applications.

Automation doi: 10.3390/automation7010030

Authors: Mouna Zerzeri Intissar Moussa Adel Khedher

In response to the need for cost-effective and resilient drivetrain architectures in renewable energy emulation platforms, this paper proposes a wind turbine emulator (WTE) designed to enhance the operational efficiency of variable-speed wind turbines (WTs) connected to electric generators in power grid applications. The proposed emulator relies on a robust sensorless vector-controlled induction motor (IM) drive fed by a reduced-switch soft–voltage source inverter (Soft-VSI) topology. The proposed control chain combines a second-order super-twisting sliding-mode flux observer, based on stator measurements, with a modified MRAS speed estimator whose Popov hyperstability offers explicit PI tuning and ensures stable sensorless speed convergence. The complete WTE design, from the aerodynamic model to the Soft-VSI induction motor drive, is implemented and evaluated in MATLAB/Simulink environment. A Mexican hat wind speed profile is used to excite the emulator and assess its dynamic behavior under diverse transient conditions. The simulation results demonstrate fast convergence of the estimated flux and speed, stable closed-loop operation when using the estimated speed, and strong robustness against no-loaded and loaded operations and rotor-resistance variations. Moreover, a comparative analysis between the proposed control scheme and a conventional first-order sliding-mode flux observer is carried out to highlight the enhanced flux and speed estimation accuracy, reduced chattering, and improved dynamic robustness of the WTE. The proposed framework provides a flexible tool to support the energy transition through the development of advanced wind energy system control strategies.

Automation doi: 10.3390/automation7010029

Authors: Fatemeh Mosleh Ali Hamidi Hamidreza Jahromi Md Ahad

Increased global energy consumption contributes to higher operational costs in the energy sector and results in environmental deterioration. This study evaluates the effectiveness of integrating Internet of Things (IoT) sensors and machine learning techniques to predict adaptive thermostat setpoints to support behavior-aware Heating, Ventilation, and Air Conditioning (HVAC) operation in residential buildings. The dataset was collected over two years from 2080 IoT devices installed in 370 zones in two buildings in Halifax, Canada. Specific categories of real-time information, including indoor and outdoor temperature, humidity, thermostat setpoints, and window/door status, shaped the dataset of the study. Data preprocessing included retrieving data from the MySQL database and converting the data into an analytical format suitable for visualization and processing. In the machine learning phase, deep learning (DL) was employed to predict adaptive threshold settings (“from” and “to”) for the thermostats, and a gradient boosted trees (GBT) approach was used to predict heating and cooling thresholds. Standard metrics (RMSE, MAE, and R2) were used to evaluate effective prediction for adaptive thermostat setpoints. A comparative analysis between GBT ”from” and “to” models and the deep learning (DL) model was performed to assess the accuracy of prediction. Deep learning achieved the highest performance, reducing the MAE value by about 9% in comparison to the strongest GBT model (1.12 vs. 1.23) and reaching an R2 value of up to 0.60, indicating improved predictive accuracy under real-world building conditions. The results indicate that IoT-driven setpoint prediction provides a practical foundation for behavior-aware thermostat modeling and future adaptive HVAC control strategies in smart buildings. This study focuses on setpoint prediction under real operational conditions and does not evaluate automated HVAC control or assess actual energy savings.

Automation doi: 10.3390/automation7010028

Authors: Adetayo Onososen Innocent Musonda

Human–robot collaboration (HRC) offers a significant potential to improve productivity, safety, and performance in construction, yet its adoption remains constrained by interrelated barriers. The existing studies largely identify these barriers in isolation, with limited insight into their systemic interactions. This study addresses this gap by synthesising prior research using PRISMA and applying interpretive structural modelling (ISM) to examine the hierarchical and causal relationships among barriers to HRC in construction. Eight barrier categories are identified: financial, safety, communication, robot technology-related, organisational, legal/regulatory, education/training, and social and human factors. The ISM–MICMAC results reveal regulatory and communication barriers as key upstream drivers shaping downstream safety, training, organisational, and technological outcomes. By moving beyond descriptive listings, the study provides a systems-level framework that supports the strategic prioritisation of interventions and informed decision-making. The findings advance the theoretical understanding of HRC as a socio-technical system and offer an evidence-informed foundation for context-sensitive implementation strategies in construction.

Automation doi: 10.3390/automation7010027

Authors: Murad Ali Khan Qazi Waqas Khan Ji-Eun Kim SeungMyeong Jeong Il-yeop Ahn Do-Hyeun Kim

Reliable environmental monitoring in remote or sparsely instrumented regions is hindered by the cost, maintenance demands, and inaccessibility of dense physical sensor deployments. To address these challenges, this study introduces VSG-SGL, a unified virtual sensor generation framework that integrates Sparse Gaussian Process Regression (SGPR) and Bayesian Ridge Regression (BRR) with deep generative learning via Variational Autoencoders (VAE) and Conditional Tabular GANs (CTGAN). Real meteorological datasets from multiple South Korean cities were preprocessed using thresholding and Isolation Forest anomaly detection and evaluated using distributional alignment (KDE) and sequence-learning validation with BiLSTM and BiGRU models. Experimental findings demonstrate that VAE-augmented virtual sensors provide the most stable and reliable performance. For temperature, VAE maintains predictive errors close to those of BRR and SGPR, reflecting the already well-modeled dynamics of this variable. In contrast, humidity and wind-related variables exhibit measurable gains with VAE; for example, SGPR-based wind speed MAE improves from 0.1848 to 0.1604, while BRR-based wind direction RMSE decreases from 0.1842 to 0.1726. CTGAN augmentation, however, frequently increases error, particularly for humidity and wind speed. Overall, the results establish VAE-enhanced VSG-SGL virtual sensors as a cost-effective and accurate alternative in scenarios where physical sensing is limited or impractical.

Automation doi: 10.3390/automation7010026

Authors: Huda Hussein Dhiaa Muhsen

Due to their complexity and nonlinearity, metaheuristic algorithms have become the standard in problem solving for problems that cannot be solved by standard computational solutions. However, the global performance of these algorithms is strongly linked to the population structuring and the mechanism of replacing the worst solutions within the population. In this paper, an Adaptive Artificial Hummingbird Algorithm (AAHA), a new version of the basic AHA, is introduced and designed to enhance performance by studying the impacts of different population initialization methods within a broad and continual migration form. For the initialization phase, four methods—the Gaussian chaotic map, the Sinus chaotic map, opposite-based learning (OBL), and diagonal uniform distribution (DUD)—are proposed as an alternative to the random population initialization method. A new strategy is proposed as a replacement for the worst solution in the migration phase. The new strategy uses the best solution as an alternative to the worst solution with simple and effective local search. The proposed strategy stimulates exploitation and exploration when using the best solution and local search, respectively. The proposed AAHA is tested through various benchmark functions with different characteristics under many statistical indices and tests. Additionally, the AAHA results are benchmarked against those of other optimization algorithms to assess their effectiveness. The proposed AAHA outperformed alternatives in terms of both speed and reliability. DUD-based initialization enabled the fastest convergence and optimal solutions. These findings underscore the significance of initialization in metaheuristics and highlight the efficacy of the AAHA for complex continuous optimization problems.

Automation doi: 10.3390/automation7010025

Authors: Marcio Luis Munhoz Amorim Oswaldo Hideo Ando Junior Mario Gazziro João Paulo Pereira do Carmo

This paper presents Nexus, a proof-of-concept low-cost, modular, and reprogrammable multichannel data logger aimed at validating the architectural feasibility of an open and scalable acquisition platform for scientific instrumentation. The system was conceived to address common limitations of commercial data loggers, such as high cost, restricted configurability, and limited autonomy, by relying exclusively on widely available components and open hardware/software resources, thereby facilitating reproducibility and adoption in resource-constrained academic and industrial environments. The proposed architecture supports up to six interchangeable acquisition modules, enabling the integration of up to 20 analog channels with heterogeneous resolutions (24-bit, 12-bit, and 10-bit ADCs), as well as digital acquisition through multiple communication interfaces, including I2C (two independent buses), SPI (two buses), and UART (three interfaces). Quantitative validation was performed using representative acquisition configurations, including a 24-bit ADS1256 stage operating at sampling rates of up to 30 kSPS, 12-bit microcontroller-based stages operating at approximately 1 kSPS, and 10-bit operating at 100 SPS, consistent with stable real-time acquisition and visualization under proof-of-concept constraints. SPI communication was configured with an effective clock frequency of 2 MHz, ensuring deterministic data transfer across the tested acquisition modules. A hybrid data management strategy is implemented, combining high-capacity local storage via USB 3.0 solid-state drives, optional cloud synchronization, and a 7-inch touchscreen human–machine interface based on Raspberry Pi OS for system control and visualization. Power continuity is addressed through an integrated smart uninterruptible power supply, which provides telemetry, automatic source switching, and limited backup operation during power interruptions. As a proof of concept, the system was functionally validated through architectural and interface-level tests, demonstrating stable communication across all supported protocols and reliable acquisition of synthetic and biosignal-like waveforms. The results confirm the feasibility of the proposed modular architecture and its ability to integrate heterogeneous acquisition, storage, and interface subsystems within a unified open-source platform. While not intended as a finalized commercial product, Nexus establishes a validated foundation for future developments in modular data logging, embedded intelligence, and application-specific instrumentation.

Automation doi: 10.3390/automation7010024

Authors: Ioana-Miruna Vlasceanu João Sarraipa Ioan Sacala Janetta Culita Mircea Segarceanu

Smart control technologies that can manage the complexity of urban traffic while also reducing response times for emergency vehicles are necessary. This article proposes AETM (Adaptive and Equitable Traffic Management), an adaptive and equitable traffic management system that integrates contextual methods for handling emergencies with traffic light control based on reinforcement learning. The system uses Q-learning to optimize traffic light phases under normal traffic conditions and integrates a dedicated emergency vehicle module, which includes detection, dynamic rerouting and contextual preemption functions. The system adaptively optimizes traffic light phases under normal traffic conditions and integrates a specialized module for emergency vehicles, which ensures their detection, dynamic rerouting and contextual preemption. The priority level is evaluated through an auxiliary fuzzy mechanism, based on interpretable rules, which takes into account local conditions without influencing the learning process. The performance of the framework is evaluated in a microscopic simulation environment by comparing classical control, adaptive control, and the full AETM configuration. The results highlight significant reductions in travel times and stops for emergency vehicles while maintaining overall traffic stability.

Automation doi: 10.3390/automation7010023

Authors: Firas Abdulrazzaq Raheem Alaq F. Hasan Enass H. Flaieh Amjad J. Humaidi

Exoskeleton knee-assistance (EKA) systems are wearable robotic technologies designed to rehabilitate and improve impaired mobility of the lower limbs. Clinical exercises are conducted on disabled patients based on physically demanding tasks which are prescribed by expert physicians. In order to carry out good tracking of the desired tasks, efficient controllers must be designed. In this study, a novel control framework is introduced to improve the robustness characteristics and tracking precision of EKA systems. The control approach integrates a super-twisting sliding mode controller (STSMC) with a nonlinear disturbance observer (NDO) to ensure robust and precise tracking of the knee joint trajectory. An evaluation of the proposed system is conducted through numerical simulations under the influence of external disturbances. The findings reveal considerable enhancements to trajectory tracking accuracy and disturbance rejection when compared to conventional STSMCs and sliding mode perturbation observer (SMPO)-based STSMCs.

Automation doi: 10.3390/automation7010022

Authors: Yijun Zhang Zonglin Zou Ku Du

This paper investigates the bipartite consensus problem for multi-agent systems subject to both switching dynamics and external disturbances within an event-triggered control (ETC) framework. The investigation commences with an analysis of time-invariant systems to establish bipartite consensus, and subsequently expands the framework to accommodate the complexities of switched systems. In time-invariant systems, agents update their states only when the event-triggering threshold is exceeded; the convergence of this mechanism can be rigorously established via an error dynamics mode. For switched systems, the system state is also updated solely when the event-triggering condition is met. Once all subsystems are stabilized, we design an appropriate mean sojourn time to mitigate state jumps caused by switching, thus ensuring bipartite consensus. Finally, four case studies based on numerical simulations to verify the theoretical results.

Automation doi: 10.3390/automation7010021

Authors: Seung Hwangbo Sherrilene Classen Sandra Winter

As the population ages, autonomous shuttles (AS) present a potential solution for older adults’ mobility needs. However, acceptance—often assessed through hypothetical scenarios rather than lived experience—remains a significant barrier. This study aimed to explore older adults’ perceptions of AS through pre- and post-exposure, and to examine how these experiences shape their AS acceptance within the Diffusion of Innovations (DOI) framework. Using existing qualitative data from pre- and post-exposure focus groups, with 32 older adults across Florida, we used hybrid thematic analysis, grounded in DOI theory. The results revealed that the technology’s ease of use, as experienced when riding the AS (Trialability), reduced initial concerns related to Complexity. While participants acknowledged the Relative Advantage of AS in enhancing their mobility and safety, their acceptance was conditional upon addressing the AS’s slow speed and abrupt braking. Acceptance was also contingent upon Compatibility with personal lifestyles and the establishment of clear AS Regulations, to build trust. The findings indicate that for older adults, AS acceptance is a dynamic process where direct exposure is essential for overcoming initial concerns. However, widespread adoption will ultimately be influenced by AS performance, seamless integration of AS into their daily lives, and a robust regulatory framework.

Automation doi: 10.3390/automation7010020

Authors: Adeyemisi Gbadebo Faraj Altumi Chaozhi Liang D T Pham

Product disassembly, critical in remanufacturing, often involves removing screws and bolts, which can be challenging due to degradation, such as rust or thread damage. Here, we develop a digital twin integrated with a Model Predictive Controller to optimise robotic screw unfastening. Using real-time force and torque data from a robot unscrewing an electric vehicle battery pack, the controller predicts and adjusts screwdriver position and spindle speed to minimise applied torque and force. Experimental results demonstrate that this approach improves unscrewing success rates and reduces torque variability compared to manual methods. These findings suggest that combining digital twin technology with MPC can enhance the efficiency and reliability of robotic disassembly processes, supporting sustainable remanufacturing efforts.

Automation doi: 10.3390/automation7010019

Authors: Arturo Tadeo Espinoza Fraire José Armando Sáenz Esqueda Isaac Gandarilla Esparza Jorge Alberto Orrante Sakanassi

To build a comprehensive academic or scientific foundation in control theory, developing the theoretical foundation is essential; however, it is equally crucial to validate the theory through practical or experimental verification. Therefore, it is necessary to have platforms that support the learning of automatic control theory. This paper proposes a fixed bi-rotor testbed as an educational tool to help undergraduate and graduate students verify control theories related to electronic engineering and automatic control systems. To evaluate the performance of the fixed bi-rotor testbed, three linear control laws are introduced: Proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Additionally, three nonlinear control techniques are examined: Backstepping, Nested Saturations, and First-Order Sliding Modes (SMC). The linear and nonlinear controller gains have been adjusted through several heuristic experiments. In multiple tests, the PD and backstepping control laws performed better than the other control techniques on the fixed bi-rotor testbed.

Automation doi: 10.3390/automation7010018

Authors: Jiehui Gao Huabo Liu

This study presents a robust estimation approach for linear discrete-time systems subject to parametric uncertainties. To address model mismatch, the proposed method enhances the MHE framework, thereby improving estimation accuracy. Based on this framework, the estimator is derived by minimizing the expected estimation error. A detailed derivation is provided, along with a novel recursive formulation for the pseudo-covariance of the estimation error. The resulting estimator maintains structural similarity to the Kalman filter and supports recursive implementation. Theoretical analysis establishes convergence to a stable system, with guaranteed boundedness and asymptotic unbiasedness of the estimation error. Simulation results demonstrate that the proposed strategy maintains high effectiveness and robustness under different uncertain conditions.

Automation doi: 10.3390/automation7010017

Authors: Jesús Anselmo Fortoul-Díaz Luis Antonio Carrillo-Martinez Javier Cuatepotzo-Hernández Froylan Cortes-Santacruz Juan Daniel Marín-Segura

Integrating emerging Industry 4.0 technologies into smart factories has been widely discussed, particularly challenges regarding the practical use of a blockchain; one remaining challenge is the role of a blockchain beyond logistics and traceability, as well as its ability to support explicit trust measurement in real industrial environments. Existing studies often treat trust as a conceptual or cloud-oriented construction, without linking it to measurable production events. This study proposes a blockchain service-level agreement (SLA) to measure trust at an open-source frugal smart factory (SF). Trust is defined as a dynamic quantitative score derived from measurable process events, including estimated and response times, assembly correctness, and transaction outcomes; all of this is calculated through a smart contract implemented on a blockchain network. The approach is implemented in a tangram puzzle assembly process that integrates cyber-physical systems, edge computing, artificial intelligence, cloud computing, data analytics, cybersecurity, and the blockchain within a unified SF architecture. The framework was experimentally validated across four representative assembly scenarios: (i) the SF delivered the puzzle in time and was correctly assembled (λs = 0.1734), (ii) the puzzle was completed within tolerance time (λs = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (λs = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (λs = 4.91 × 10−5); demonstrating that the model accurately estimates expected assembly times and updates trust without manual intervention during a physical manufacturing task, addressing the limitations of prior conceptual and cloud-based approaches. The main research contributions include an operational SLA-based trust model, the demonstration of the feasibility of applying blockchain-based SLAs in a physical SF environment, and evidence that a blockchain can be justified as a mechanism for managing and measuring trust in SF, rather than solely for traceability or logistics.

Automation doi: 10.3390/automation7010016

Authors: Hitoshi Yoshioka Hirotada Hashimoto Akihiko Matsuda

This study presents a human-navigable ship-handling support system that employs artificial intelligence (AI) for survey line tracking. AI was developed using the Deep Deterministic Policy Gradient (DDPG), a type of deep reinforcement learning (DRL), and was evaluated through experiments conducted with a research vessel. The experiments revealed several issues inherent to DRL that required improvement. The first issue was the asymmetry observed in the policy learned through the DDPG. To address this, a learning approach that utilizes symmetric training data and symmetry-constrained actor and critic neural networks was proposed. The second issue was excessive steering during tracking maneuvers. To mitigate this, an objective function for actor learning that incorporates a cost term to suppress the magnitude of actions was proposed. The third issue was the frequent oscillation of actions. To resolve this, improved conditioning for action policy smoothing was introduced in the objective function to smooth actions appropriate to the situation. A subsequent experiment at sea was conducted to evaluate the improved AI-based ship-handling support system. As a result, precise path tracking performance with minimal operator discomfort and smooth control actions was achieved through manual ship handling guided by AI-generated instructions under actual sea conditions.

Automation doi: 10.3390/automation7010015

Authors: Wei Song Junying Min Jiaqi Zhao

Recently, these conventional object detection methods have certain defects that must be overcome, such as insufficient detection accuracy in complex scenes and low computational efficiency. Then, this paper proposes a Star operation and Restormer-based YOLOv11 model that leverages global context for vehicle detection (SRG-YOLO), which aims to enhance both detection accuracy and efficiency in complex environments. Firstly, during the optimization of YOLOv11n architecture, a Star block is introduced. By enhancing non-linear feature representation, this Star block improves the original C3K2 module, thereby strengthening multi-scale feature fusion and consequently boosting detection accuracy in complex scenarios. Secondly, for the detection heads of YOLOv11n, Restormer is incorporated via the improved C3K2 module to explicitly leverage spatial prior information, optimize the self-attention mechanism, and augment long-range pixel dependencies of YOLOv11n. This integration not only reduces computational complexity but also improves detection precision and overall efficiency through more refined feature modeling. Thirdly, a Context-guided module is integrated to enhance the ability to capture object details using global context. In complex backgrounds, it effectively combines local features with their contextual information, substantially improving the detection robustness of YOLOv11n. Finally, experiments on the VisDrone2019, KITTI, and UA-DETRAC datasets illustrate that SRG-YOLO achieves superior vehicle detection accuracy in complex scenes compared to conventional methods, with particular advantages in small object detection.

Automation doi: 10.3390/automation7010014

Authors: Ahmed R. El-Sawi Amir Almslmany Abdelrhman Adel Ahmed I. Saleh Hesham A. Ali Mohamed M. Abdelsalam

This research proposes the design and implementation of an adaptive optical system (AOS) for monitoring low-orbit satellites (LOSs) to ensure that they do not deviate from their pre-planned path. This is achieved by designing a telescope with an optical system that contains six mirrors in a regular hexagonal shape; the side length of one mirror is 30 cm, and there is also a spectral analyzer system in the middle to separate the spectra emitted by stars from those reflected from low-orbit satellites. A SwinTrack-Tiny (STT) is used, with modifications using temporal information via insertion. The model incorporates a new purpose-built image update template as a third input to the model and combines the attributes of the new image with the attributes of the primary template via an attention block. To maintain the dimensions of the original model and take advantage of its weights, an attention block with four vertices is used.

Automation doi: 10.3390/automation7010013

Authors: Prajakta Salunkhe Atharva Tilak Mahesh Shirole Ninad Mehendale

Gas leak detection in industrial environments poses critical safety challenges that require algorithms capable of balancing rapid source identification with comprehensive spatial coverage. Conventional approaches using fixed sensor networks provide limited coverage, while manual inspection methods expose personnel to hazardous conditions. This paper presents a novel Gradient-Guided Depth-First Search (GG-DFS) algorithm designed for autonomous mobile robots, which integrates gradient-following behavior with systematic exploration guarantees. The algorithm utilizes local concentration gradient estimation to direct movement toward leak sources while implementing depth-first search with backtracking to ensure complete environmental coverage. We assess the performance of GG-DFS through extensive simulations comprising 160 independent runs with varying leak configurations (1–4 sources) and starting positions. Experimental results show that GG-DFS achieves rapid initial source detection (9.3±7.3steps;mean±SD), maintains 100% coverage completeness with 100% detection reliability, and achieves 50% exploration efficiency. In multi-source conditions, GG-DFS requires 70% fewer detection steps in four-leak scenarios compared to single-leak environments due to gradient amplification effects. Comparative evaluation demonstrates a substantial improvement in detection speed and efficiency over standard DFS, with GG-DFS achieving a composite performance score of 0.98, compared to 0.65 for standard DFS, 0.64 for the lawnmower pattern, and 0.53 for gradient ascent. These findings establish GG-DFS as a robust and reliable framework for safety-critical autonomous environmental monitoring applications.

Automation doi: 10.3390/automation7010012

Authors: Saadi Turied Kurdi Luttfi A. Al-Haddad Ahmed Ali Farhan Ogaili

Autonomous navigation for agricultural UAVs faces persistent challenges due to atmospheric disturbances such as wind direction, temperature gradients, and pressure variations, which can lead to significant deviations from planned flight paths. This study presents a deep learning-based navigation approach that integrates geographic information systems (GIS) with deep neural networks (DNNs) to improve energy efficiency and trajectory accuracy in agricultural UAV operations. To simulate realistic environmental disturbances, actual flight data from an Iraqi Airways short-haul route (Baghdad–Istanbul–Baghdad) were utilized. These trajectories were affected by both tailwinds and headwinds and were analyzed and modeled to train a DNN capable of predicting and correcting path deviations. The optimized system was then tested in a simulated agricultural UAV context. Results show that for tailwind conditions (Baghdad–Istanbul), the GIS-DNN model reduced fuel consumption by 610 L and flight time by 31 min compared to actual conditions. In headwind conditions (Istanbul–Baghdad), the model achieved a 558 L fuel saving and reduced the flight time by 28 min. Based on these results, it can be concluded that deep learning integrated with GIS can significantly enhance UAV path optimization for improved energy efficiency and mission reliability in precision agriculture.

Automation doi: 10.3390/automation7010011

Authors: Ahmed G. Mahmoud A. Aziz Saleh Al Dawsari Amr E. Rafaat Ayat G. Abo El-Magd Ahmed A. Zaki Diab

Robotics is increasingly used in higher education laboratories, but most commercial robots are costly and designed for industrial use. This paper presents the design, modeling, and experimental evaluation of a low-cost four-degree-of-freedom (DOF) SCARA robot for educational and research purposes. The robot supports pick-and-place and laser engraving tasks. Direct and inverse kinematics were developed using Denavit–Hartenberg parameters, and the mechanical structure was validated through the dynamic analyses. A new machine learning (ML) framework integrating Support Vector Machine (SVM) and Random Forest (RF) models was implemented to enhance motion precision, predict task success, and compensate positioning errors in real time. Experimental tests over 360 cyles under varying speeds, payloads, and object types show that the SVM predicts grasp success with 94.4% accuracy, while the RF model estimates XY positioning error with an RMSE of 1.84 mm and cycle time error with an RMSE of 0.41 s. Moreover, a novel approach in this work that combines it with a laser engraving machine has been suggested. Repeatability experiments report 0.97 mm ISO-standard repeatability, and laser engraving trials yield mean positional errors of 0.45 mm, with maximum deviation of 0.90 mm. Compared to a baseline PID controller, the ML-enhanced strategy reduces RMS positioning error from 3.30 mm to 1.83 mm and improves repeatability by 36.5%, while slightly decreasing cycle time. These results demonstrate that the proposed SCARA robot achieves high-precision, consistent, and flexible operation suitable for both academic and light-duty practical applications.

Automation doi: 10.3390/automation7010010

Authors: Nurettin Çerçi Yaprak Yalçın

The primary objective of this research is to develop a probabilistic nonlinear model predictive control structure (NMPC) that efficiently operates uncrewed surface vehicles (USVs) in an environment that has probabilistic disturbances, such as wind, waves, and currents of the water, while simultaneously maneuvering the vehicle in a way that avoids stationary or moving stochastic obstacles in its path. The proposed controller structure considers the mean and covariances of the inputs or state variables of the vehicle in the cost function to handle probabilistic disturbances, where an extended Kalman filter (EKF) is utilized to calculate the mean, and the covariances are calculated dynamically via a linear matrix equality based on this mean and obtained system matrices with successive linearization for every sampling instance. The proposed control structure deals with non-zero-mean probabilistic disturbances such as water current via an innovative approach that treats the mean of the disturbance as a deterministic part, which is estimated by a disturbance observer and eliminated by a control term in the controller in addition to the control signal obtained via MPC optimization; the effect of the remaining zero-mean part is handled over its covariance during the probabilistic MPC optimization. The probabilistic constraints are also dealt with by converting them to deterministic constraints, as in linear probabilistic MPC. However, unlike the linear MPC, these constraints updated each sampling instance with the information obtained via successive linearization. The control structure incorporates the velocity obstacle (VO) method for collision avoidance. In order to ensure stability, the proposed NMPC adopts a dual-mode strategy, and a stability analysis is presented. In the second mode, an LQG design that ensures stability in the existence of non-zero mean disturbance is also provided. The simulation results demonstrate that the proposed probabilistic NMPC framework effectively handles probabilistic disturbances as well as both stationary and moving obstacles, ensuring collision avoidance while reaching the desired position and orientation through optimal path tracking, outperforming the conventional NMPC.

Automation doi: 10.3390/automation7010009

Authors: Rodrigo Gimenez-Valenzuela Julio Montesdeoca Brayan Saldarriaga-Mesa Flavio Capraro Daniel Patiño

In modern industry, continuous production systems require the integration of monitoring systems capable of real-time inspection and anomaly detection of final products. This necessitates high-speed capture of product images and rapid information processing to determine the rejection of defective products. To address the challenges of reducing processing time and increasing fault recognition accuracy in products, a novel detection method based on image analysis and subsequent classification is proposed. While the techniques employed, such as image histograms and Principal Component Analysis, are well-established in image and data processing, the innovative integration of these methods in this approach provides a streamlined and highly efficient solution for classification. Specifically, the classification process relies on prior image processing, where the histograms of the 3 color channels of each image are obtained and concatenated, then PCA is applied, resulting in separable clusters. Cluster classification is achieved through a simple SVM. A significant advantage of this method is that it requires a reduced amount of image data for training the SVM, simplifying this stage of the process. The proposed method is benchmarked using a dataset of images aimed at detecting defects in a pill blister pack, which may include missing pills, while a data augmentation process is implemented. The relationship between the image histogram and the presence of faults is demonstrated under controlled lighting and sensor arrangement environments.

Automation doi: 10.3390/automation7010007

Authors: Arthur Kniphoff da Cruz Christian Siemers Lorenz Däubler Ana Clara Hackenhaar Kellermann

In the classic energy sector, as well as in the manufacturing and process industries, Programmable Logic Controller (PLC) systems are used for electrical substation control. However, PLCs frequently do not support the communication protocols defined on the standard International Electrotechnical Commission (IEC) 61850. Therefore, this paper presents a vendor-independent method for the integration of Protection and Control (P&C) Intelligent Electronic Devices (IEDs), components of the substation bay level, in PLCs from the substation station level. The method can be used with legacy and modern controllers that offer an open communication interface, where the use of Transmission Control Protocol/Internet Protocol (TCP/IP) is supported. Since many legacy systems offer an open communication interface, this method makes it possible to reuse PLCs, bringing cost efficiency and ecological benefits. The method can be used in a single or redundant way since redundancy is always required in power distribution control. A prototype was developed for the integration over IEC 61850 Manufacturing Message Specification (MMS), and its functional validation is presented in this paper. This solution, besides reducing hardware and software acquisition costs, also contributes to a reduction in electronic waste (E-Waste) and the achievement of Sustainable Development Goals (SDGs).

Automation doi: 10.3390/automation7010008

Authors: Batyrbek Suleimenov Olga Shiryayeva Dmitriy Gorbunov

This study proposes an intelligent control algorithm for multiple-input multiple-output (MIMO) industrial processes. This algorithm is based on the integration of a digital twin (DT), model predictive control (MPC), a genetic algorithm (GA), and a neural network (NN). The developed architecture employs a hybrid MPC scheme incorporating an additional NN correction branch. The workflow includes input data pre-processing, operating point linearization and NN training, computation of the optimal control sequence over a receding horizon, closed-loop control and adaptation based on prediction error. This innovative hybrid control law uses a linear state-space model as the base predictor and a compact NN superstructure to compensate for unmodeled nonlinearities. The GA searches for the optimal sequence of control actions while respecting process constraints and ensuring stable use of the NN correction. The methodology was tested on a phosphoric acid purification process. Compared to baseline MPC, the proposed algorithm increased purification efficiency to 95.1%, reduced the integral tracking error by 11.4%, and decreased the control signal amplitude by 10–15%. Selecting the appropriate reagent supply and vacuum modes ensured stable operation despite fluctuations in the raw material. These results confirm the effectiveness of DT-based hybrid control in applications requiring precision, adaptability, and strict constraint compliance. The approach is scalable and can be applied to other continuous production systems within Industry 4.0 initiatives.

Automation doi: 10.3390/automation7010006

Authors: Mohamed A. A. Ismail Saadi Turied Kurdi Mohammad S. Albaraj Christian Rembe

Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard sensors or similar methods, which typically require continuous data acquisition and non-negligible onboard computational resources. This study presents a portable Laser Doppler Vibrometer (LDV)-based system designed for noncontact, offboard, and high-sensitivity measurement of UAV vibration signatures. The LDV measurements are analyzed using a Deep Extreme Learning-based Neural Network (DeepELM-DNN) capable of identifying both propeller fault type and severity from a single 1 s measurement. Experimental validation on a commercial quadcopter using 50 datasets across multiple induced fault types and severity levels demonstrates a classification accuracy of 97.9%. Compared to conventional onboard sensor-based approaches, the proposed framework shows strong potential for reduced computational effort while maintaining high diagnostic accuracy, owing to its short measurement duration and closed-form learning structure. The proposed LDV setup and DeepELM-DNN framework enable noncontact fault inspection while minimizing or eliminating the need for additional onboard sensing hardware. This approach offers a practical and scalable diagnostic solution for large UAV fleets and next-generation smart agricultural and industrial aerial robotics.

Automation doi: 10.3390/automation7010005

Authors: Abid Mohammad Ali Petro Mushidi Tshakwanda Henok Berhanu Tsegaye Harsh Kumar Md Najmus Sakib Raddad Almaayn Ashok Karukutla Michael Devetsikiotis

We study UAV-assisted 5G uplink connectivity for disaster response, in which a UAV (unmanned aerial vehicle) acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink non-orthogonal multiple access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic line of sight/non-line of sight (LoS/NLoS), realistic receiver noise levels and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, and edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2–10.1 percentage points compared to strong baselines (OFDMA with heuristic placement, PSO-based placement/power, and PPO without NOMA) while reducing median UE transmit power by 64.6%. The results are averaged over at least five random seeds, with 95% confidence intervals. Ablations isolate the gains from NOMA, adaptive SIC order, and bounded-action parameterization. We discuss robustness to imperfect SIC and CSI errors and release code/configurations to support reproducibility.

Automation doi: 10.3390/automation7010004

Authors: Nopparut Khaewnak Soontaree Seangsri Siripong Pawako Sorada Khaengkarn Jiraphon Srisertpol

This research presents a study on enhancing the localization and orientation accuracy of indoor Autonomous Guided Vehicles (AGVs) operating under a centralized, camera-based control system. We investigate and compare the performance of two Extended Kalman Filter (EKF) configurations: a standard EKF and a novel Blended EKF. The research methodology comprises four primary stages: (1) Sensor bias correction for the camera (CAM), Dead Reckoning, and Inertial Measurement Unit (IMU) to improve raw data quality; (2) Calculation of sensor weights using the Inverse-Variance Weighting principle, which assigns higher confidence to sensors with lower variance; (3) Multi-sensor data fusion to generate a stable state estimation that closely approximates the ground truth (GT); and (4) A comparative performance evaluation between the standard EKF, which processes sensor updates independently, and the Blended EKF, which fuses CAM and DR (Dead Reckoning) measurements prior to the filter’s update step. Experimental results demonstrate that the implementation of bias correction and inverse-variance weighting significantly reduces the Root Mean Square Error (RMSE) across all sensors. Furthermore, the Blended EKF not only achieved a lower RMSE in certain scenarios but also produced smooth trajectories similar to or less than the standard EKF in some weightings. These findings indicate the significant potential of the proposed approach in developing more accurate and robust navigation systems for AGVs in complex indoor environments.

Automation doi: 10.3390/automation7010003

Authors: Lin Wang Binjie Zhang Qinyan Tan Dejun Duan Yulei Wang

Foggy weather critically undermines the autonomous perception capabilities of unmanned aerial vehicles (UAVs) by degrading image contrast, obscuring object structures, and impairing small target recognition, which often leads to significant performance deterioration in conventional detection models. To address these challenges in automated UAV operations, this study introduces Hazy Aware-YOLO (HA-YOLO), an enhanced detection framework based on YOLO11, specifically engineered for reliable object detection under low-visibility conditions. The proposed model incorporates wavelet convolution to suppress haze-induced noise and enhance multi-scale feature fusion. Furthermore, a novel Context-Enhanced Hybrid Self-Attention (CEHSA) module is developed, which sequentially combines channel attention aggregation (CAA) with multi-head self-attention (MHSA) to capture local contextual cues while mitigating global noise interference. Extensive evaluations demonstrate that HA-YOLO and its variants achieve superior detection precision and robustness compared to the baseline YOLO11, while maintaining model efficacy. In particular, when benchmarked against state-of-the-art detectors, HA-YOLO exhibits a better balance between detection accuracy and complexity, offering a practical and efficient solution for real-world autonomous UAV perception tasks in adverse weather.

Automation doi: 10.3390/automation7010002

Authors: Nitish Katal Soumya Ranjan Mahapatro Pankaj Verma

Quantitative Feedback Theory (QFT) enables the control system to guarantee stability and performance in the presence of plant uncertainty, thus offering a quantitative and less conservative framework for designing robust yet practical controllers. The presented work investigates a single-stage constraint optimization-based approach for synthesizing controllers for the ship roll stabilization. The typical QFT loop shaping is a manual two-stage procedure that demands a proficient understanding of loop-shaping principles on Nichols charts. The proposed procedure simplifies the QFT synthesis process by introducing a single-stage method that allows for concurrent synthesis of both the QFT controller and pre-filter. The present work considers the synthesis of fractional order controllers (using the FOMCON toolbox). The proposed method also enables the designer to pre-specify the controller architecture at the beginning of the design procedure. A comparative analysis with the controllers obtained using the QFT toolbox, Ziegler–Nichols, H∞, IMC, and MPC have also been presented in the work. The implementation has been carried out for the ship roll stabilization, which is one of the critical problems in marine engineering, as it directly impacts the vessel safety, operational efficiency, and passenger comfort, wherein excessive roll can lead to reduced propulsion efficiency. The obtained results highlight that the proposed controller performs better than the benchmark controllers, and Monte Carlo simulations have also been included to support the results.

Automation doi: 10.3390/automation7010001

Authors: Bandita Sahu Indrajeet Kumar

Enhancing the serviceability of mobile robots is an important factor for improving regular work to a great extent. This approach has been implemented in areas such as industry, healthcare, and military. To ensure the successful implementation of the proposed work, it is important to have an impeccable collision-free path for mobile robots. This goal has been accomplished by developing an intelligent fault management system. The proposed work produces an efficient path through the use of a hybrid algorithm that combines the benefits of the sine cosine algorithm (SCA) and particle swarm optimization (PSO) algorithms. The proposed work reports on the object transportation by a pair or group of robots from source to destination, and the mentioned task can be proficiently completed in three steps: fault identification, fault resolution using robot replacement, and computation of a collision-free path. The proposed work was successfully implemented in a C language environment to showcase its competence in terms of execution time, path traveled, and path deviated. The presented comparative analysis of the proposed algorithm demonstrates the effectiveness of the approach in terms of several metrics, such as path planning, cooperation, and fault management. The proposed approach achieved path optimality by reducing the traveled path by approximately 9.6% compared to QCOV-R and 8.4% compared to the ABCO algorithm in an environment with a minimum of eight obstacles.