Search Results (209)

Background/Objectives: An individual with a hand disability, whether caused by an accident, disease, or congenital condition, may have significant problems with their daily activities, self-perception, and ability to work. Prosthetic hands can be used to restore essential hand functions, and features such as adaptive grasps can enhance their usability. Due to noise in the sEMG signal and hardware limitations in the system, reliable myoelectric control remains a challenge for low-cost prosthetics. ESP32 microcontrollers are used in this study to develop an SVM-based sEMG classifier that addresses these issues and improves responsiveness and accuracy. A 3D-printed mechanical structure supports the prosthesis, reducing production costs and making it more accessible. Methods: The prosthetic hand is developed using an ESP32 as the microcontroller, a Myoware Muscle Sensor to detect muscle activity, and an ESP32-based control system that integrates sEMG acquisition, SVM classification, and finger actuation with FSR feedback. A surface electromyography (sEMG) method is paired with a Support Vector Machine (SVM) algorithm to help classify signals from the sensor to improve the user’s experience and finger adaptability. Results: The SVM classifier achieved 89.10% accuracy, an F1-score of 0.89, and an AUC of 0.92, with real-time testing demonstrating that the ESP32 could reliably distinguish flexion and extension signals and actuate the servo, accordingly, producing movements consistent with the kinematic simulations. Complementing this control performance, the prosthetic hand was constructed using a coupled 4 bar linkage mechanism fabricated in PLA+, selected for its superior factor of safety compared to the other tested materials, ensuring sufficient structural reliability during operation. Conclusions: The results demonstrate that SVM-based sEMG classification can be effectively implemented on low-power microcontrollers for intuitive, low-cost prosthetic control. Further work is needed to expand beyond two-class detection and increase robustness against muscle fatigue and sensor placement variability. Full article

Osteoarthritis (OA) is a progressive musculoskeletal disorder that affects not only older adults but also younger populations, often leading to chronic pain, joint stiffness, functional impairment, and a decline in quality of life. Non-invasive physical rehabilitation plays a critical role in slowing disease progression, alleviating symptoms, and maintaining joint mobility. However, rehabilitation tools such as compression gloves and manual exercise aids are typically passive and provide minimal real-time feedback to patients or clinicians. Others, such as exoskeletons and soft-actuated devices, can be costly or complex to use. This study presents the design and development of an electrically actuated glove integrated with force and flex sensors, intended to assist individuals diagnosed with Stage 2 OA in performing guided finger exercises. The system integrates a digital front-end application that offers real-time feedback and data visualization, enabling more personalized and trackable therapy sessions for both patients and healthcare providers. Preliminary results from an initial human trial with healthy participants demonstrate that the glove enables naturalistic movement without imposing excessive restriction or augmentation of motion. These findings support the glove’s potential in preserving hand coordination and dexterity, key objectives in early-stage OA intervention, and suggest its suitability for integration into home-based or clinical rehabilitation protocols. Full article

High-precision position control and pressure control are core performance requirements for modern electro-hydraulic actuators. While the design of controllers for high-performance position servo systems is relatively straightforward, the development of pressure control strategies for electro-hydraulic actuators poses substantially greater challenges. This is primarily due to the fact that unknown time-varying parameters, load dynamics, and sensor-induced measurement noise within the system drastically deteriorate the performance of the closed-loop system. To address these challenges, this study proposes an adaptive output feedback pressure controller specifically tailored for electro-hydraulic servo systems. This controller not only exhibits insensitivity to dynamic load disturbances but also effectively mitigates the adverse effects of time-varying parameters and sensor measurement noise. Theoretical analysis demonstrates that the proposed controller can guarantee the asymptotic stability of the system’s tracking error. Furthermore, detailed simulation and experimental results are presented to validate the superiority of the designed controller over conventional control strategies. Full article

System time delays and nonlinear unmodeled dynamics severely constrain the control performance of the Active Suspension Electro-Hydraulic Servo Actuator (ASEHSA). To tackle these challenges, this paper presents a Dynamic Error Differentiation-based Model-Free Adaptive Control (DE-MFAC) strategy integrated with an Improved Particle Swarm Optimization (IPSO) algorithm. Established under the Model-Free Adaptive Control (MFAC) framework, the DE-MFAC integrates a dynamic error differentiation mechanism and an implicit expression of time delays, thus removing the dependence on a precise system model. The traditional PSO algorithm is improved by incorporating an inertia weight adjustment strategy and a boundary reflection wall strategy, which effectively mitigates the issues of local optima and boundary stagnation. In AMESim 2021, a 1/4 vehicle active suspension electro-hydraulic actuation system model is constructed. To ensure an impartial evaluation of controller performance, the IPSO algorithm is employed to optimize the parameters of the PID, MFAC, and DE-MFAC controllers, respectively. Co-simulations with Simulink 2023b are conducted under two time delay scenarios using a composite square-sine wave signal as the reference. The results indicate that all three IPSO-optimized controllers realize effective position tracking. Among them, the DE-MFAC controller exhibits the optimal performance, demonstrating remarkable advantages in reducing tracking errors and balancing settling time with overshoot. These findings verify the effectiveness of the proposed control strategy, time delay compensation mechanism, and optimization algorithm. Future research will involve validation on a physical ASEHSA platform, further exploration of the method’s applicability and robustness under diverse operating conditions, and extension to other industrial systems with similar nonlinear time delay features. Full article

In the modern industrial context, many manufacturers design universal testing machines (UTMs) equipped with servo-hydraulic or electromechanical linear actuators, which offer excellent control capabilities and high-quality force signal measurement, at the expense of high costs due to the need for hydraulic power units or dedicated electrical networks. The complexity of these systems discourages manufacturers of mechanical components, especially the ones produced through additive manufacturing (AM), from investing in machines for the determination of mechanical properties according to international standards, settling instead for information derived from technical datasheets of the base material (filament or powders), which rarely include information about fatigue life. Within this context, the Fast Fatigue Machine (FFM), designed by KnoWow srl and ItalSigma srl, makes mechanical characterization of materials a process accessible to any organization that may require it. This was made possible by designing a pneumatic benchtop testing machine with a built-in setup for Thermographic Methods (TMs) usage. The aim of this work is to validate pneumatic actuators as a viable alternative to servo-hydraulic systems, demonstrating their effectiveness and reliability. Frequency analysis on both sinusoidal waveforms, root mean square error (RMSE) evaluation, and percentage total harmonic distortion (THD%) calculations showed that, while the servo-hydraulic system closely follows the load signal with a THD of around 5%, regardless of the applied load intensity, the pneumatic system exhibits higher distortion (THD of approximately 9%, strongly dependent on the load levels) and a high-frequency harmonic component, which, however, does not affect the overall results. Life cycle assessment (LCA) analysis confirmed the convenience of the pneumatic system and TMs in material testing and fatigue characterization. Full article

Electro-hydraulic servo fatigue testing machines are susceptible to concurrent degradation and failure of multiple components during high-frequency, high-load, and long-duration cyclic operations, posing significant challenges for online health monitoring. To address this, this paper proposes a multi-state recognition method based on spatiotemporal feature fusion and bidirectional cross-attention. The method employs a Bidirectional Temporal Convolutional Network (BiTCN) to extract multi-scale local features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture forward and backward temporal dependencies, and Bidirectional Cross-Attention (BiCrossAttention) to achieve fine-grained bidirectional interaction and fusion of spatial and temporal features. During training, GradNorm is introduced to dynamically balance task weights and mitigate gradient conflicts. Experimental validation was conducted using a real-world multi-sensor dataset collected from an SDZ0100 electro-hydraulic servo fatigue testing machine. The results show that on the validation set, the cooler and servo valve achieved both accuracy and F1-scores of 100%, the motor-pump unit achieved an accuracy of 98.32% and an F1-score of 97.72%, and the servo actuator achieved an accuracy of 96.39% and an F1-score of 95.83%. Compared to single-task models with the same backbone, multi-task learning improved performance by approximately 3% to 4% for the hydraulic pump and servo actuator tasks, while significantly reducing overall deployment resources. Compared to single-task baselines, multi-task learning improves performance by 3–4% while reducing deployment parameters by 75%. Ablation studies further confirmed the critical contributions of the bidirectional structure and individual components, as well as the effectiveness of GradNorm in multi-task learning for testing machines, achieving an average F1-score of 98.38%. The method also demonstrated strong robustness under varying learning rates and resampling conditions. Compared to various deep learning and fusion baseline methods, the proposed approach achieved optimal performance in most tasks. This study provides an effective technical solution for high-precision, lightweight, and robust online health monitoring of electro-hydraulic servo fatigue testing machines under complex operating conditions. Full article

Upper limb disabilities resulting from stroke affect millions worldwide, yet current rehabilitation systems face limitations in portability, cost-effectiveness, and multi-joint integration. This study presents a cable-driven parallel exoskeleton integrating elbow, wrist, and finger assistance into a single portable device. The design strategically separates actuation components, housing all motors in a backpack unit, while limb-mounted modules serve as cable routing guides, achieving seven degrees of freedom within practical constraints of portability (1.2–1.5 kg) and cost-effectiveness (3D-printed components). The device incorporates seven servo motors controlled via Arduino with IMU feedback and PID algorithms. Kinematic and dynamic analyses informed mechanical design, while ARMAX system identification enabled controller optimization achieving 87.96% model fit. Experimental validation with eight healthy participants performing four upper limb exercises demonstrated consistent trends toward reduced activation in four monitored agonist muscles with exoskeleton assistance (21.3% average reduction, p = 0.087), with moderate effect sizes for proximal muscles (Cohen’s d = 0.70–0.79) and significant reductions in brachioradialis during radial/ulnar deviation (23.4%, p = 0.045). These findings provide preliminary evidence of the device’s potential to reduce muscular effort during assisted movements, warranting further clinical validation with patient populations. Full article

The electrification of non-road mobile machinery is advancing to enhance sustainability and reduce emissions. This study investigates how to maximize the efficiency of the retrofitting of a material handler from an internal combustion engine to a battery-powered electric motor, while keeping the hydraulic system unchanged. Using a previously validated model, this study proposes three control strategies for the electric motor and hydraulic pump to enhance efficiency and performance. The first control strategy optimizes hydraulic pump performance within its most efficient displacement range. The second strategy maximizes powertrain efficiency by considering both efficiencies of the electric motor and hydraulic pump. The third strategy uses a servo-actuated valve to adjust the load-sensing margin and exhibits energy savings up to 14.2% and an 11.5% increase in efficiency. The proposed strategies avoid complex optimization algorithms, ensuring practical applicability for small- and medium-sized enterprises, which often face cost constraints and limited scalability. Full article

This research aims to investigate the practicality and feasibility of pneumatic muscle actuators (PMAs) applied in the pneumatic servo system. The mechanism consists of closed symmetrical planar robotic arms driven by two pairs of opposing PMAs, whose structure is similar to human arms. Importantly, the two distal links (or wrist parts) are combined into a collective end-effector, whose desired position is controlled only by the two shoulder angle joints. When two pairs of PMAs are attached to the upper arms, they actuate each shoulder and assist in the movement of the arms. However, the nonlinear behavior, high hysteresis, low damping, and time-varying characteristics of PMAs significantly limit their controllability. Therefore, to effectively address these challenges, a Fourier series-based adaptive sliding mode controller with $H_{\mathrm{\infty }}$ (FSB-ASMC + $H_{\mathrm{\infty }}$) is employed to achieve accurate path tracking of the PMAs. This control approach not only compensates for approximation errors, disturbances, and unmodeled dynamics but also ensures the desired $H_{\mathrm{\infty }}$ positioning performance of the overall system. The controller method can not only effectively prevent approximation errors, disturbances, and un-modeled dynamics but can also ensure the required $H_{\mathrm{\infty }}$ positioning performance of the whole system. Thus, the results of the experiment showed that the control strategy for the system collocating the FSB-ASMC + $H_{\mathrm{\infty }}$ can attain excellent control performance. Full article

Real-time hybrid simulation (RTHS) evaluates the dynamic performance of a structure by physically testing the selected components while modeling the remaining structure numerically, making it efficient in both cost and testing space requirements. In RTHS, accurately imposing target boundary conditions on specimens is critical, as it directly influences test accuracy and overall simulation stability. However, boundary condition application often experiences tracking errors due to the dynamics of the servo–hydraulic loading system and control-structural interaction. This challenge intensifies with multiple actuators operating in a multi-axial setup, introducing dynamic coupling effects. Thus, an outer-loop controller enabling precise actuator tracking of reference boundary conditions is essential for reliable RTHS. While advancements in outer-loop controllers for uniaxial RTHS exist, multi-axial RTHS (maRTHS) employing multiple degrees of freedom control remains underexplored. This study applies the adaptive discrete feedforward controller (ADFC), consisting of a discrete feedforward compensator and an online identifier, to a multi-input, multi-output (MIMO) system for maRTHS. To validate ADFC’s performance and robustness, 1000 virtual maRTHS tests incorporating plant uncertainties were conducted under seismic excitations. Ten evaluation criteria were applied. Results confirm that ADFC achieves robust and stable control by reducing phase and amplitude errors, while also improving estimation accuracy at the physical–numerical interface. Full article

Artificial muscles play a key role in the future of humanoid robotics and medical devices, with research on wire-driven joints leading the field. While electric servo motors were once at the forefront, the focus has shifted toward materials that react to changes in the environment (smart materials), including pneumatic silicone actuators and temperature-reactive metallic alloys, aiming to replicate human muscle actuation for improved performance. Initially designed for rigid actuators, control strategies were adapted to address the unique dynamics of artificial muscles. Although current controllers offer satisfactory performance, further optimization is necessary to mimic natural muscle control more rigorously. This study details the design and implementation of a novel system that mimics biological muscle. This system is designed to replicate the full range of motion and control functionalities, which can be utilized in various applications. This research has three significant contributions in the field of sustainable soft robotics. First, a novel shape memory alloy-based linear actuator is introduced, which achieves significantly higher displacements compared to traditional SMA wire-driven systems through a guiding mechanism. Second, this linear actuator is integrated into a hybrid soft actuation structure, which features a silicone PneuNet as the end effector and a force sensor for real-time pressure feedback. Lastly, a hardware Spiking Neural Network (HW-SNN) is utilized to control the exhibited force at the actuator’s endpoint. Experimental results showed that the displacement with the control system is significantly higher than that of the traditional control-based shape memory alloy systems. The system evaluation demonstrates good performance, thus advancing actuation and control in humanoid robotics. Full article

Although electro-hydrostatic actuators (EHAs) hold broad application prospects in more-electric aircraft and high-end equipment, they face a difficult trade-off between dynamic response and energy efficiency. To simultaneously enhance the dynamic response and energy efficiency of the EHA, this paper designs an innovative variable pump displacement and variable motor speed (VPVM) configuration that utilizes an electro-hydraulic servo valve for active displacement control. To address the flow mismatch problem associated with traditional asymmetric single-rod cylinders without reducing the power density of EHA, this paper also designs an innovative symmetric single-rod cylinder configuration. Based on the above two innovative configurations, this paper further develops a corresponding EHA prototype with a rated power density of 0.72 kW/kg. Simulation and experimental results demonstrate that compared to the traditional EHA with the fixed pump displacement and variable motor speed configuration (FPVM-EHA), the EHA with the proposed VPVM configuration (VPVM-EHA) not only improves energy efficiency and reduces motor heat generation under low-speed and heavy-load conditions, but also achieves a dynamic response close to that of the FPVM-EHA under fast dynamic response conditions. Full article

Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings. Full article

Cable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) with a single monocular camera, asking whether simple global static Jacobians (GSJ) can be sufficient and how an adaptive Jacobian estimator behaves. Two platforms are evaluated: a three-cable (3C) platform and a redundant six-cable du-al-plane platform (RC). Motion-capture (MoCap) validation shows that redundancy improves stability and tracking by reducing platform tilt and making image errors correspond more directly to Cartesian motions. Across static and low-speed tracking tasks, GSJ proved reliable, while a baseline recursive least-squares (RLS) estimator without safety triggers was often unstable. These findings suggest that improving mechanical conditioning may be as important as adding algorithmic complexity, and that carefully estimated global models can suffice in practice. Limitations include the use of a single camera, laboratory conditions, and a baseline RLS variant; future work will evaluate event-triggered adaptation and higher-speed trajectories. Full article

This paper presents the design, implementation, and experimental results of a six-degree-of-freedom Delta-type parallel manipulator, in which all actuators were realized using proprietary pneumatic muscles. The objective of the study was to evaluate the suitability of this type of actuator for applications in parallel robotics, with particular attention to their dynamic properties, nonlinearities, and potential limitations. In the first part of the article, the details of the manipulator’s construction and the kinematic model, covering both the forward and inverse kinematics, are presented. The control system was based on antagonistic pairs of pneumatic muscles forming servo drives responsible for the motion of individual arms. The experimental investigations were focused on analyzing trajectory-tracking accuracy and positioning repeatability, both in unloaded conditions and under additional payload applied to the end-effector. The results indicate that positioning errors for simple trajectories were generally below 1 mm, whereas for complex trajectories and under load, they increased, particularly during changes in motion direction, which can be attributed to friction and hysteresis phenomena in the muscles. Repeatability tests confirmed the ability of the manipulator to repeatedly reach the desired positions with small deviations. The analysis carried out confirms that pneumatic muscles can be effectively applied to drive parallel manipulators, offering advantageous features such as high power density and low mass. At the same time, the need for further research on nonlinearity compensation and durability enhancement was demonstrated. Full article