Search Results (588)

This research communication presents an analysis of dynamic supply chains (DSCs). The main goal of model-based cybertronics is to approximate, via a mathematical model from a dynamical system, the dynamics and behavior of dynamic supply chains. This considers that is at the operational level, where automation and control theory approaches take an insight —in this case, via Lyapunov stability—as a way to extend the use of mechatronic systems. Three case studies are presented: Firstly, the mathematical modeling and stability analysis of the ball-and-beam problem, as an approximation of a two echelon supply chain. Secondly, the mathematical modeling and stability analysis of a cold chain with temperature monitoring, and its relationship to inventory levels, are presented. From a theoretical perspective, applying model-based cybertronics in DSCs has direct practical implications: it improves operational control, enhances decision-making, and optimizes inventory management, particularly in cold chains. By treating high-volume supply chains as dynamical systems, managers can anticipate fluctuations and quantify efficiency. Finally, Lyapunov stability analysis ensures that models reliably reflect real-world behavior, enabling automation and predictable performance at an operational level in DSCs. Full article

This paper proposes an energy-based training assistance controller for a unicycle riding simulator inspired by Human Adaptive Mechatronics (HAM). We focus on sagittal plane (pitch) balance for beginners and derive a simplified longitudinal plane unicycle model, where pedaling is represented as an action–reaction torque between the wheel and the rider–saddle body. After time normalization, the saddle dynamics is expressed in a form suitable for energy analysis. Using the natural Hamiltonian of the uncontrolled system, we design a minimum-intervention pumping–damping controller that modifies the energy flow only when necessary. The assistance is smoothly activated outside a training core region defined by a saddle-angle bound: a damping term suppresses excessive motion, and a pumping term prevents trapping in a tilted posture when the energy becomes too small. The proposed framework offers physically interpretable, localized assistance while preserving the natural unicycle dynamics required for skill learning. Full article

Modern machine tools are safety-critical mechatronic systems, yet shop floor maintenance from abnormal events still relies heavily on scarce expert know-how and time-consuming manual searches across heterogeneous controller documentation. This paper presents an on-premise multimodal AI assistant. It integrates large language models with retrieval augmented generation and real-time machine signals to support operator-in-the-loop fault diagnosis. The proposed system provides three tightly coupled functions: (1) alarm-grounded guidance, which answers controller alarms and recommends corrective actions by grounding generation on manuals, maintenance procedures, and historical alarm cases; (2) parameter-aware reasoning, which injects live process and health indicators (e.g., spindle temperature, vibration, and axis states) into the reasoning context through an industrial data pipeline, enabling context specific troubleshooting; and (3) vision enabled support, which retrieves similar visual cases and generates concise visual instructions when text alone is insufficient. The assistant is deployed within an intranet environment to satisfy industrial security and privacy requirements and is orchestrated via lightweight tool calling for seamless integration with existing shop floor systems. Experiments on real machine tool alarm scenarios demonstrate that the proposed system achieves 82% answer correctness for alarm Q&A and improves response consistency and time-to-resolution compared with baseline keyword search and template-based guidance. The results suggest that grounded, multimodal chatbot assistants can act as practical AI-based feedback and decision support mechanisms for mechatronic production equipment, bridging human skill gaps while enhancing reliability and maintainability. Full article

DC motors are widely used in mechatronic systems; however, their performance degrades significantly in the presence of nonlinear mechanical loads, parameter variations and sensing uncertainties. This paper proposes three control strategies (i.e., PID, optimal, and hybrid controllers) for discrete-time DC motor systems to overcome the disturbances caused by nonlinear mechanical loads and parameter variations. Optimal control of nonlinear discrete-time systems is formally characterized by the Hamilton–Jacobi–Bellman (HJB) equation, whose analytical solution is generally intractable. To address this challenge, a learning-based optimal control strategy based on the Heuristic Dynamic Programming (HDP) framework is developed to approximate the HJB equation, supported by a formal convergence proof. For that purpose, Neural Networks (NNs) are employed to approximate both the cost function and the optimal control policy, enabling near-optimal performance with manageable computational complexity. Although the resulting optimal control achieves fast convergence, it may introduce overshoot and steady-state offset under nonlinear disturbances. To address this limitation, a hybrid control framework is proposed, where nonlinear optimal corrections are integrated with the robustness and adaptability of Proportional–Integral–Derivative (PID) control through error-dependent gating and gain-scheduling mechanisms. A structured evaluation framework is conducted, including nominal analysis, motor-parameter stress testing across nine nonlinear scenarios, controller-design sensitivity analysis, and stochastic measurement-noise assessment under filtered sensing conditions. Results demonstrate that the hybrid controller preserves transient speeds within 5–10% of the optimal controller while effectively eliminating overshoot and steady-state offset under nominal conditions. The hybrid design reduces the accumulated tracking error by more than 95% compared to the optimal controller, while incurring only negligible additional control effort. Under aggressive supply-sag disturbances, the hybrid controller significantly limits peak deviation and reduces accumulated tracking error by over 90%, while maintaining comparable control cost. Overall, the hybrid framework provides a convergence-proven and practically deployable control solution that combines near-optimal convergence speed with robust, overshoot-free performance for intelligent motion-control and robotics applications. Full article

This paper presents an original positional pneumatic actuator for long-stroke coordinate mechanisms. The design integrates a rodless pneumatic cylinder, a jet control system, and an external braking device. It achieves a positioning accuracy of 200 microns, a discrete step of 2 mm, and an average speed of 0.15 m/s over a maximum stroke of 6 m. This solution offers a two-fold improvement in technical, economic, and operational performance compared to electromechanical drives. A mathematical model of the drive was developed using SimInTech software and validated with a custom-built experimental stand. The discrepancy between calculated and experimental data does not exceed 18%. The study established the dependence of positioning accuracy on the load and kinematic characteristics of the drive, which helps reduce design time for coordinate mechanisms. As a result of the research, a new scheme of a positional pneumatic actuator has been developed and experimentally confirmed, which allows for a two-fold improvement in technical and economic indicators compared to electromechanical analogs due to the original combination of a rodless cylinder, a jet control system, and an external braking device. Full article

Biodegradable nanocomposite materials possessing self-healing behavior are emerging as an attractive option of being used in advanced mechatronic systems. The current study is focused on a thorough examination of the micromechanical properties of graphene–reinforced polylactic acid (PLA)/polycaprolactone (PCL) composite samples, followed by estimation of their self-healing behavior upon heating. Polymer blend–based nanocomposite materials were prepared using the green and reliable in terms of good nanofiller dispersion melt extrusion method. 3D printed nanocomposite specimens with impeccable flatness were subjected to fine microscratch testing by applying a constant force experimental mode. The surface resistance of the three-component polymer materials against the lateral movement of the stylus fulfilling the scratch and the impact of the dual-phase PLA/PCL ratio on the nanocomposite mechanical performance were estimated by calculation of the coefficient of friction (COF = F_x/F_z). COF values in the range of 0.8–1.4 indicated excellent nanocomposite resilience against scratch. Creating a heterogeneous polymer system that combines phase-separated soft and hard domains with close melt and glass transition temperatures, respectively, may facilitate the physical flow of macromolecular chains into voids or free volume areas. This aspect can be critical in the achievement of thermally–induced self-healing properties of the composite material. Scanning electron microscopy (SEM) imaging of the microscratches, made before and after Joule heating of the polymer samples, revealed a significant degree of surface recovery and a sensible reduction in the width of the adjusted scratch grooves. Full article

A support is a machine element that transmits loads to the base or other structures. A simple support is designed to withstand forces acting in the longitudinal direction, and a flexible support is designed to withstand forces in both longitudinal and transverse directions. The possibilities for the use of flexible supports are very wide. In precision mechanics, flexible supports are used in positioning systems, micropositioning systems, vibration damping systems, as well as in fastening applications requiring adjustment and other structural configurations. The main problem of flexible supports is ensuring stability. This work examines the dependence of the stiffness of supports used in mechanical and mechatronic systems on the material and dimensions of the flexible element. A theoretical analysis of the stiffness of flexible supports, finite element method (FEM) modeling, and experimental stiffness research were performed. A special stand was manufactured for experimental research. A research methodology was developed, according to which experimental research was carried out. After theoretical, FEM and experimental research, the results obtained were compared and conclusions were formulated. The obtained data can be practically used in the research and design of new flexible supports that ensure desired stability, as well as in the improvement of existing support structures. Full article

Gas turbines are complex mechatronic systems that require reliable dynamic models to support automated operation under varying aerodynamic conditions. This study presents a novel dynamic surge modeling framework that integrates compressor variable geometry into a gas turbine component-level model. A physics-based formulation is developed in which the influence of inlet guide vane (IGV) deflection is incorporated through sensitivity-based parameterization and a physics-informed extension of compressor performance characteristics. The proposed framework captures the nonlinear interaction between compressor surge dynamics and component-level system behavior, enabling consistent prediction of instability onset and dynamic stability margins over a wide range of operating conditions. Model verification through stability analysis, phase-space characterization, and time-domain simulations demonstrates that the framework reproduces key features of classical compressor surge and quantifies the impact of variable geometry on system stability. The results show that the proposed model provides a practical and computationally efficient basis for control-oriented surge analysis, including stability monitoring and surge delay assessment. By coupling the IGV-aware surge dynamics with a gas turbine component-level model, the proposed method enables control-oriented, automation-ready simulation for gas turbine design and control. Full article

Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With rising system complexity, the effort required to ensure that all relevant conditions for each test target are identified increases. Manual test-case definition remains the norm, creating effort bottlenecks in ensuring systematic coverage and compliance with standards such as ISO 26262 and ISO 29119. This paper explores how large language models (LLMs) can support the identification of complex boundary conditions for CPS test cases through detailed requirement analysis. The impact of performing taxonomy-guided, structured requirement mapping prior to test-case generation was evaluated by comparing it with a version without this guidance. Furthermore, the influence of supplying a Model-Based Systems Engineering (MBSE) system model as context information via Graph RAG is examined. The results show that structured, stepwise reasoning significantly improves reliability and consistency over unguided generation, while system-model information provides valuable contextual insight but has a minor impact in the chosen example. These findings outline a scalable framework for AI-assisted test-case generation. Full article

The paper presents the design of an active seat suspension system for a medium-sized passenger vehicle (installation height of 180 mm), which is aimed at enhancing passenger comfort, with an emphasis on autonomous vehicle applications. The system is developed in two design variants based on Scott–Russell and Kempe mechanisms. The former is characterized by high rigidity and low friction, and it serves as a benchmark solution in this research. The latter is distinguished by cost-effectiveness and, thus, targeted for production vehicle applications once it is verified against the benchmark solution. Both designs are developed to satisfy the operational requirements derived from system computer simulations (suspension stroke of ±40 mm, speed of up to 0.5 m/s, and acceleration of up to 1 g), which are based on a half-car vehicle model extended with seat suspension dynamics and controlled by a linear quadratic regulator. The paper also outlines the electrical, measurement, and basic control subsystem of the overall active seat suspension mechatronic system. Finally, it presents experimental identification results to illustrate that the designed system complies with the specified requirements. Full article

To address the limitations of existing quarter-vehicle models in capturing pitch motion and front-rear coupling effects, this paper proposes a half-vehicle mechatronic suspension system based on the electromechanical analogy. Traditional methods often overlook non-ideal effects and the dynamic interaction between the front and rear wheels. This paper constructs an equivalent electrical network model for the half-vehicle suspension system. To ensure the physical realizability of the system, parameter optimization is performed under positive-real constraints using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This approach achieves an optimal trade-off between vertical vibration suppression and pitch control. Simulation results under random road input at a vehicle speed of 20 m/s indicate that while the unconstrained mechatronic suspension improves ride comfort, it increases the dynamic tire load by 19.18%. In contrast, the constrained mechatronic suspension reduces RMS vertical body acceleration by 19.54% and pitch angular acceleration by 2.22% compared to the standard passive suspension. Additionally, a reduction of 8.29% was observed in the suspension working space RMS, alongside a 1.26% decrease in the dynamic tire load. These results demonstrate that introducing appropriate positive-real constraints effectively balances ride comfort and road-holding performance, providing a systematic modeling and optimization framework for half-vehicle mechatronic suspensions. Full article

Lubrication performance dominates the rating life of grease-lubricated pitch bearings. Conventionally, the life modification factor is determined using base oil viscosity, whose validity is rarely verified. This work presents an effective viscosity-based method for life evaluation of wind turbine pitch bearings. The effective viscosity of grease is measured under actual operating conditions, and a comparative study is conducted against the conventional base oil viscosity method. The rationality of the proposed approach is validated by bearing life tests. Results indicate that the life modification factor calculated from effective viscosity agrees significantly better with test data. Adopting effective viscosity can substantially improve the accuracy of bearing life prediction. The proposed method provides a reliable and practical way to assess the lubrication performance and fatigue life of pitch bearings. Full article

The rapid evolution of Artificial Intelligence (AI) has significantly transformed the capabilities, performance, and autonomy of modern mechatronic systems. As industries transition toward intelligent and interconnected manufacturing environments, AI has emerged as a powerful enabler of real-time decision-making, adaptive control, predictive maintenance, and autonomous operation. This review provides a comprehensive analysis of AI integration within mechatronic systems, examining its influence on system performance, autonomy, and manufacturing efficiency. Key AI techniques including machine learning, deep learning, reinforcement learning, evolutionary optimization, and computer vision are evaluated in terms of their applications in control, sensing, diagnostics, and robotics. The paper also highlights advancements in AI-driven motion control, autonomous navigation, sensor fusion, and smart factory operations. Critical challenges such as data requirements, computational constraints, system interoperability, and safety concerns are discussed to identify research gaps. Finally, emerging trends and future directions, such as edge AI, digital twins, explainable AI, and fully autonomous mechatronic cells, are explored. This review consolidates current knowledge and provides insights to guide researchers and practitioners in developing next-generation intelligent mechatronic systems capable of supporting the demands of Industry 4.0 and beyond. Full article

Remote photoplethysmography (rPPG) enables noncontact estimation of vital signs, particularly heart rate, by analyzing subtle periodic skin color variations in facial videos. While deep learning has advanced rPPG signal extraction, existing methods rely on carefully designed task-specific architectures that are costly to develop and generalize poorly. In this work, we demonstrate that the general video foundation model VideoMAE v2 can be effectively adapted to the rPPG signal regression task by introducing only a lightweight adapter, without modifying its pretrained backbone. We freeze the entire VideoMAE v2 encoder and introduce a Temporal Difference Convolutional Adapter to capture the subtle interframe intensity differences. To address the mismatch between VideoMAE v2′s short input window (16 frames) and the long temporal context typically required for robust rPPG extraction (e.g., 160 frames), we adopt an overlapping sliding window strategy for segmented inference and reconstruct the full signal through weighted temporal aggregation. On the COHFACE and UBFC-rPPG datasets, our method achieves mean absolute errors (MAEs) of 0.90 and 1.55, reducing the error by more than 55% and 42%, respectively, compared to PhysFormer (2.00 and 2.70). Furthermore, on challenging real-world datasets such as BUAA-MIHR, which features strong illumination variations, and VIPL-HR, which involves significant head movements, our approach achieves MAEs of 6.68 and 8.23, respectively, despite incorporating no task-specific robustness modules. These results demonstrate stable rPPG signal recovery and validate the feasibility of leveraging general video foundation models for physiological signal perception. Full article

The Fourth Industrial Revolution reshapes manufacturing and workforce demands, yet a persistent gap remains between industry needs and engineering education. While proficiency in industrial robotics, adaptive control, and automation becomes critical, traditional education struggles to bridge the theory–practice divide. This systematic review examines technology transfer from factory to classroom to develop authentic Industry 4.0 competencies. Following PRISMA 2020 guidelines, we synthesized 52 empirical studies (2019–2025) focusing on technology complexity, pedagogical approaches, and learning outcomes. Random-effects meta-analysis of 12 representative studies reveals large positive effects: Hedges’ g of 0.786 (95% CI: 0.726–0.846, p < 0.001) with homogeneous effects ($I^2$ = 0.00%, p = 0.464), indicating robust generalizability. However, critical gaps emerged: only 7.7% employ actual industrial manipulators versus educational kits, adaptive control pedagogy remains limited, and fault-tolerant systems teaching receives minimal attention. Technology complexity analysis reveals clear progression from educational kits through semi-industrial platforms to industrial systems, with significant differential effects on transferable skills (r = 0.68, p < 0.001). This study proposes the ARC Framework integrating technology taxonomy, competency progression, pedagogical strategies, and assessment rubrics. Cost–effectiveness analysis demonstrates remote labs optimize impact-per-investment ratios ($45 vs. $280 per student), providing an evidence-based framework for technology transfer in engineering education. Full article