Automation, Volume 7, Issue 4 (August 2026) – 29 articles

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

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

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

Accurate monitoring of atmospheric ammonia (NH${}_3$) is important for air-quality assessment and sustainable agriculture, but available datasets differ in spatial resolution, temporal coverage, units, and physical meaning. This study presents a feature-based machine-learning framework with train–test leakage-controlled preprocessing to evaluate relative NH${}_3$ classification consistency across three datasets over China: CAMS GEI, CAMS EAC4, and MEIC. Statistical descriptors were extracted for four spatial–temporal cases: Province–Year, Zone–Year, Province–Season–Year, and Zone–Season–Year. Six classifiers were evaluated using chronological testing and cross-validation. CAMS GEI provided the broadest spatial–temporal coverage, while MEIC showed comparatively stable classifier behavior. The best-performing ensemble models commonly achieved accuracies between 0.97 and 0.99 in the main province-level cases. Mean Absolute Value (MAV) was the leading feature in several province-level analyses, with a maximum reported contribution of 64.26%. These scores describe the separability of threshold-derived reference classes and should not be interpreted as an independent physical prediction of NH${}_3$ from external atmospheric drivers. Full article

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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. Full article