Automation, Volume 7, Issue 1 (February 2026) – 35 articles

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. Full article

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. Full article

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 V_dc/6, a significant improvement over the V_dc/2 typically observed with the standard DTC approach. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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 (${\lambda }_s$ = 0.1734), (ii) the puzzle was completed within tolerance time (${\lambda }_s$ = 0.0649), (iii) the puzzle was delivered on time and was incorrectly assembled (${\lambda }_s$ = 0.0005), and (iv) the puzzle was completed outside the tolerance time and was correctly assembled (${\lambda }_s$ = 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. Full article

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. Full article

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. Full article

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. Full article

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 $(9.3\pm 7.3\mathrm{steps};\mathrm{mean}\pm \mathrm{SD})$, 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. Full article

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. Full article

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. Full article

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. Full article

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). Full article

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. Full article

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. Full article

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. Full article