Search Results (149)

This work presents an experimental comparison of three speed control strategies for a permanent magnet DC (PMDC) rover actuator implemented on a resource-constrained embedded microcontroller platform. The system operates under fixed-rate discrete control with quantized encoder velocity feedback, representative of low-cost embedded systems. The controllers evaluated are a classical PID, a PID controller designed via discrete pole placement, and a Mamdani fuzzy controller. Beyond conventional tracking and transient response metrics, the proposed evaluation framework incorporates jerk-based kinematic indicators to assess the mechanical activity induced by control actions under both nominal and mechanically disturbed operating conditions. Experimental validation was performed over a range of operating speeds using repeated trials, and the observed differences were evaluated through nonparametric statistical testing. The results show that controller rankings depend strongly on operating conditions: the classical PID provides smoother motion under nominal conditions, whereas the fuzzy and compensated PID controllers achieve superior disturbance rejection when external mechanical perturbations are introduced. These findings reveal a clear tradeoff between mechanical smoothness and tracking robustness, and demonstrate that controllers exhibiting better tracking performance do not necessarily produce the smoothest kinematic response. The principal contribution of this work is the experimental demonstration that jerk-based indicators provide essential complementary information to conventional performance metrics for the evaluation and selection of embedded speed controllers in mechatronic systems subject to variable mechanical loading. Full article

Background/Objectives: In laparoscopic and robot-assisted surgery, bleeding may rapidly impair operative-field readability and procedural safety. In the broader Robin Heart teleoperation framework, interpretation of such events is relevant not only for scene understanding but also as a potential prerequisite for future safety-oriented supervisory functions under communication-degraded conditions. The aim of this study was to assess whether a deep learning model for blood segmentation could provide outputs suitable for preliminary image-level temporal characterization of visible blood-region behavior in laparoscopic video. Methods: A U-Net-based binary blood-segmentation model was implemented in-house in PyTorch and evaluated on three paired image–mask datasets: a simulated bleeding dataset prepared under controlled laboratory conditions, an internal operative laparoscopic dataset, and an external-domain subset derived from the public GynSurg dataset. Segmentation performance was assessed using 5-fold cross-validation and reported using the Dice coefficient and Intersection over Union (IoU). Training dynamics were analyzed using training and validation loss and Dice curves. Additional baseline comparisons were performed on the internal operative dataset using U-Net++ and DeepLabV3+. Temporal analysis was performed on selected video fragments, including a low-motion reference sequence without active bleeding progression, internal bleeding-related sequences, and external-domain sequences, using mask-derived descriptors and auxiliary optical-flow-based motion descriptors computed after camera-motion compensation within the detected blood-related ROI. Results: In 5-fold cross-validation, the U-Net-based model achieved Dice coefficient and IoU values of 0.915 ± 0.012 and 0.851 ± 0.019 on the simulated dataset, 0.856 ± 0.013 and 0.756 ± 0.025 on the internal operative dataset, and 0.707 ± 0.053 and 0.570 ± 0.056 on the external-domain GynSurg subset, respectively. On the internal operative dataset, the proposed model performed comparably to U-Net++ and slightly above DeepLabV3+ under the same cross-validation protocol. The temporal descriptor set differentiated low-motion reference behavior, more spatially coherent progression, rapid coherent expansion, and dynamic or motion-active progression profiles. Peak dA/dt reflected abrupt visible blood-area expansion, temporal IoU described mask stability over time, and optical-flow-based descriptors provided additional information on local motion activity within the detected blood-related ROI. Conclusions: The results support the feasibility of combining deep-learning-based blood segmentation with temporal and optical-flow-based descriptors for exploratory image-level characterization of visible blood-region behavior in laparoscopic video. Within the Robin Heart development pathway, such descriptors may, in the future, serve as candidate components of image-analysis support modules for safety-oriented teleoperative scenarios. At this stage, they should be interpreted as exploratory image-derived indicators rather than clinically validated markers of bleeding severity. Full article

This paper proposes a finite-time adaptive backstepping active suspension control strategy, integrating command filtering and composite learning, to address the degradation of ride comfort and attitude stability in heavy-duty vehicles caused by shifting loads and harsh roads. First, a nonlinear dynamic vehicle model is established, treating multi-source complex disturbances as a single lumped disturbance and accounting for suspension stiffness and damping nonlinearities. To stabilize the body attitude, a tri-axis controller governing the vertical, pitch, and roll motions is developed, incorporating the practical physical constraints of actuators. By employing a composite learning Radial Basis Function neural network, the controller achieves smooth approximation and precise compensation of lumped disturbances, significantly enhancing the system’s active disturbance rejection performance under complex excitations. Furthermore, the finite-time stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Finally, the strategy is evaluated under a 40% load mass mismatch and continuous random road excitations. Results indicate that the proposed strategy effectively curbs the deterioration of suspension nonlinearities during overloads, ensuring smoother dynamic transitions across all three axes. Compared to conventional backstepping control, the proposed approach reduces the root mean square values of vertical, pitch, and roll accelerations by 19%, 13%, and 35%, respectively. Ultimately, this framework effectively improves vehicle stability and disturbance rejection, providing a robust reference for heavy-duty vehicle chassis control. Full article

Sarcopenia is an age-related condition characterized by a decline in skeletal muscle mass and function, leading to impaired mobility and an increased risk of adverse health outcomes. However, the neuromuscular mechanisms underlying gait dysfunction in sarcopenia remain incompletely understood. In this study, individuals with sarcopenia and age-matched healthy controls were recruited. Gait parameters were assessed using a motion capture system and quantified through spatiotemporal analysis, muscle activity was evaluated using surface electromyography (sEMG) with phase-specific activation metrics, and cortical activity was measured using electroencephalography (EEG) and further analyzed using spectral analysis and partial directed coherence (PDC)-based graph-theoretical measures to assess frequency-specific functional connectivity. Individuals with sarcopenia exhibited significantly reduced gait speed and shorter step length, along with prolonged loading response and pre-swing phases. Among the recorded muscles, the tibialis anterior (TA) showed significant alterations, characterized by an increased and earlier first activation peak and a reduced and delayed second peak during the gait cycle. Phase-specific analysis revealed increased TA activity during the loading response phase and decreased activity during the pre-swing phase. EEG analysis revealed beta-band-specific alterations, with increased node strength and node degree in the frontal and central regions and elevated node strength in the parietal region, while no significant differences were observed in the delta, theta, alpha, or gamma bands. These findings suggest that sarcopenia is associated with neuromuscular alterations. The coexistence of increased beta-band functional connectivity strength and persistent gait impairment may reflect inefficient compensation, in which increased neural recruitment does not fully restore gait function. These results highlight the importance of targeting neuromuscular coordination in rehabilitation. Full article

Underwater towed survey systems are widely used for marine observation, resource exploration, and target identification. While high-speed towing is increasingly required to improve operational efficiency, conventional single-stage towed systems face a critical trade-off: active heave compensation systems are complex and costly, whereas purely passive configurations lack sufficient disturbance rejection at higher speeds. To address this gap, this study proposes a two-stage towing system consisting of a vessel, heavy cable, depressor, light cable, and detection towed body, where the depressor functions as a mechanical low-pass filter. The depressor reduces vessel-induced heave motion transmission by approximately 79% compared with a conventional single-stage system. CFD simulations are conducted to evaluate hydrodynamic performance and extract key coefficients. A lumped-mass dynamic model is established for time-domain motion simulations. An integral sliding-mode controller with vessel heave feedforward compensation is designed to enhance depth-tracking capability. The active controller eliminates step response overshoot and provides robust depth regulation under wave disturbances. Sea trials under real ocean conditions validate the system’s motion stability, demonstrating satisfactory depth-keeping performance at high towing speeds. The simulation results show good agreement with experimental data, confirming the effectiveness of the proposed system and dynamic model. This work offers a practically validated towing platform solution for high-precision underwater survey operations. Full article

Complex human activities consist of sequential, simple limb movements, acting as impulse responses from the motor system. In short-term human activity recognition (ST-HAR), the inherently brief observation window results in non-stationary signals and “information starvation,” breaking the time-translational symmetry of kinetic signals. Moreover, traditional Variational Mode Decomposition (VMD) and Hilbert Transform (HT) suffer from suboptimal decomposition levels (K) and spectral asymmetry. This paper proposes an improved VMD-HT framework to enhance feature extraction from short-term Inertial Measurement Unit (IMU) signals. First, an instantaneous-frequency-driven adaptive VMD method is developed to mitigate mode mixing by automatically determining the optimal K. Second, an information-enhanced instantaneous energy density (IEIE) feature is introduced. By fusing kinetic energy from both positive and negative frequency domains, this feature restores the spectral symmetry of the energy representation, precisely quantifying fine motion variations and compensating for information loss caused by the limited temporal span. Experimental results on PAMAP2, WARD, and a self-collected dataset, NOITOM, demonstrate the method’s effectiveness. With a 0.5 s window, the proposed model achieves outstanding recognition accuracies of 93.60%, 96.41%, and 97.22%, respectively, outperforming state-of-the-art approaches in capturing transient short-term information. Full article

Neurological disorders often cause severe upper limb motor impairments that restrict independence and quality of life. Robot-assisted rehabilitation enables high-intensity, task-oriented, and quantifiable training. One key feature, gravity compensation (GC), reduces the muscular effort needed to lift the limb and supports voluntary movement by offsetting the weight of the arm. This systematic review aimed to identify the types of GC strategies used in upper limb rehabilitation robots and assess clinical evidence on their effectiveness for improving motor outcomes. A search of PubMed, Scopus, Web of Science, and IEEE Xplore (January 2005–May 2025) identified 60 eligible studies: 23 describing GC implementation and 40 reporting clinical results. GC was implemented into exoskeletons, end-effectors, and sling-suspension systems through passive mechanical designs or active, model-based, and adaptive control algorithms. However, few studies reported key technical parameters such as controller algorithms, loop frequency, or tuning procedures, and only one addressed the control system stability. Clinically, GC-assisted training improved arm movement and range of motion, with greater effects in participants with higher impairment levels. However, the functional gains were modest and not superior to conventional or other robotic therapies. Substantial heterogeneity in training protocols and participants’ demographics further limits direct comparison among GC strategies. Overall, the relative effectiveness of robot-assisted GC across devices remains unclear. Standardized reporting and more clinical trials are needed to compare GC strategies within and between different types of robots. Full article

This paper presents a hierarchical control architecture for high-precision trajectory tracking of a car-like mobile robot (CLMB) operating under external disturbances arising from normal and tangential wheel forces. The proposed solution addresses the critical challenge of simultaneously rejecting disturbances and accurately following a predefined path at a determined cruise velocity. Since the vehicle is equipped with an electronic differential at the low level, a nonlinear dynamic control (NDC) scheme is implemented to regulate the speed in each wheel. This controller actively estimates and compensates for differential traction losses and other lumped disturbances in real time, ensuring robust wheel velocity tracking across varying terrain conditions. The compensated system is then governed by a high-level frugal model predictive controller (FMPC) that leverages a dynamic vehicle model to compute optimal steering and velocity commands, thereby minimizing future trajectory-tracking errors. To achieve a precise and reliable state estimation necessary for feedback control, an Extended Kalman Filter (EKF) is designed to fuse high-frequency data from wheel encoders with absolute pose measurements from a motion capture system, mitigating the drift inherent in odometry alone. Experimental results on a physical robotic platform demonstrate tracking accuracy and robust disturbance rejection under different operating conditions. Full article

In flexible manufacturing systems, the composite mobile manipulator (CMM) is subject to nonlinear inertial disturbances arising from the dynamic coupling between the mobile platform and the robotic arm. These disturbances significantly impair positioning precision during grasping tasks. This paper addresses the dynamic decoupling of multi-body nonlinear inertial disturbances within CMM systems. Departing from the conventional “stop-then-plan” serial execution paradigm, we propose a full-cycle spatiotemporally coupled trajectory optimization method. The operation cycle is bifurcated into two synergistic stages: “dynamic calibration” and “static execution.” The dynamic calibration trajectory is pre-planned and executed synchronously during platform movement to actively compensate for inertial-induced pose deviations. Concurrently, the static execution trajectory is optimized and then triggered immediately upon platform standstill, ensuring a seamless and precise transition to the “Grasping Pose”. It is worth noting that the temporal characteristic central to this framework lies in the concurrent execution of static trajectory optimization and platform transit: by the time the platform reaches its destination, the pre-planned trajectory is already available for immediate triggering, achieving zero task-switching wait time at the planning layer. The term “zero-latency” here does not imply a fixed-cycle real-time response at the control layer, but rather the complete elimination of decision latency afforded by the parallel planning architecture. This framework eliminates computational latency, markedly enhancing operational efficiency. Key innovations include two novel constraints. First, the Adaptive Task-space Bounded Search Constraint (ATBSC) framework restricts optimization to a geometry-inspired search region, thereby enhancing search efficiency and ensuring controllable deviations. Second, the Multi-Rigid-Body Coupling Constraint (MRBCC) system explicitly models inertial transmission across motion phases to suppress pose fluctuations. The proposed framework is developed and validated within an obstacle-free workspace. In simulation-based validation on a UR10 6 degree-of-freedom manipulator model, experimental results indicate that ATBSC increases valid solution density to 84.7% and reduces average deviation by 72.8%. Furthermore, under the tested conditions, MRBCC mitigates end-effector position errors by 79.7–81.0% with a 97.5% constraint satisfaction rate. The improved Cuckoo Search algorithm (ICSA), serving as the solver component of the proposed framework, achieves an 11.9% lower fitness value and a 13.1% faster convergence rate compared to the standard Cuckoo Search algorithm in the tested scenarios, suggesting its effectiveness as a reliable solver for the constrained multi-objective trajectory optimisation problem. Full article

Proton exchange membrane fuel cells (PEMFCs) have considerable potential for frequency support in grid-forming applications. However, their transient dispatchable power is nonlinearly influenced by operating conditions, such as the oxygen excess ratio and stack temperature, thereby weakening frequency support performance by delaying power compensation during disturbances. To address this issue, a coordinated control strategy for inertia support and active power compensation is proposed that explicitly accounts for operating-state effects. Based on a dynamic PEMFC model, the effects of the oxygen excess ratio and stack temperature on transient output capability are analyzed, and a jointly corrected inertia coefficient is introduced into the virtual synchronous generator (VSG) rotor motion equation to achieve adaptive adjustment of virtual inertia under varying operating conditions. In addition, model predictive control (MPC) is incorporated into the VSG control framework, and a performance index is formulated using weighted quadratic terms of frequency variation and input power, thereby enabling the compensation power to be determined online and the PEMFC power reference to be updated accordingly. Simulation results show that the proposed strategy can effectively suppress frequency fluctuations under disturbance conditions. Compared with Conventional PI-VSG, the maximum frequency deviation and the peak rate of change of frequency (ROCOF) are reduced by 49.1% and 62.1%, respectively. Full article

The anchoring effect of drill-anchor equipment directly determines the support quality of roadways. Currently, hydraulic drill-anchor robots suffer from insufficient positioning control precision during operation, and drilling position deviations induce roadway collapse risks and serious safety hazards. Therefore, effectively improving the position control accuracy of the drill arm of drill-anchor robots is a critical prerequisite for ensuring roadway support safety. Aiming at the drill-arm position control system of drill-anchor robots, this study establishes a friction model for friction compensation based on the analysis of the motion mechanism of drill-anchor robots and then constructs mathematical models for the slewing and pitching systems respectively. To realize the precise position control of the drill arm, an active disturbance rejection predictive control scheme is proposed. An extended state observer (ESO) is adopted to observe the system states and unmodeled disturbances, and the particle swarm optimization (PSO) algorithm with an improved objective function is applied to optimize the parameters of the drill-arm position controller. Finally, simulation results demonstrate that the designed active disturbance rejection predictive control method for drill-arm positioning, based on friction compensation and PSO tuning, exhibits excellent control performance and achieves accurate trajectory tracking of the drill-arm position of drill-anchor robots. This research has important theoretical and practical significance for promoting the automatic control of drill-anchor robots in underground engineering. Full article

To investigate how knee joint protector design affects the biomechanical characteristics of knee motion under various activities, this pilot study (n = 5) examined how knee joint protector design modulates knee biomechanics across walking, jogging, squatting, and sit-to-stand tasks using optical motion capture and AnyBody musculoskeletal modeling (FullBody_GRFPrediction). We quantified knee flexion kinematics, model-estimated joint reaction forces and moments, and model-estimated muscle activity of eight lower-limb muscles under four conditions with different levels of structural constraint: no protector (Pro.off), a conventional sleeve-type protector (Pro.a), a segmented support protector (Pro.b), and a wrapping fixation protector (Pro.c). The biomechanical protective performance of the knee joint protector was task- and phase-dependent. The results showed that Pro.a optimized muscle activation. Pro.b increased sagittal-plane design but increased joint loading and muscle activity. Pro.c induced noticeable distal compensation along the kinetic chain. The findings revealed that protector effects were task-dependent. Dynamic tasks mainly affected coronal-plane stability parameters, whereas quasi-static tasks more clearly altered sagittal load distribution. In this study, biomechanical protective performance is defined as reduced knee joint loading without disproportionate increases in model-estimated muscle activity or excessive loss of functional knee flexion range. Under this definition, greater structural constraint did not consistently produce a more favorable biomechanical profile. These results provide a feasibility baseline for task-specific protector evaluation and motivate confirmatory studies with larger cohorts and experimental validation. This study provides theoretical and methodological insights to guide future design and optimization of knee joint protectors. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

Owing to its large flexible envelope, an airship is highly sensitive to environmental disturbances, such as wind gusts. Fluid–structure interaction induces structural deformation, which modifies the aerodynamic force distribution and introduces additional coupling effects. Furthermore, servo-elastic deformation alters the position and orientation of actuators mounted on the envelope, resulting in deviations between commanded and actual control forces. To address these issues, a composite control strategy integrating trajectory tracking and active elastic deformation suppression is proposed for a flexible airship equipped with multiple vectored propellers. Structural flexibility is explicitly incorporated into the dynamic model through modal decomposition, where the generalized coordinates and their time derivatives associated with deformation modes are included in the system state vector. A disturbance observer is developed to estimate actuator-level force deviations induced by elastic deformation, and the estimated disturbances are compensated in real time. Based on this formulation, a composite control framework, referred to as servo-elastic control, is established. The framework consists of a trajectory tracking controller and a displacement compensation module to achieve simultaneous motion regulation and structural deflection suppression. Numerical results demonstrate that the displacement at vectored thrust actuator attachment points is reduced to approximately 10% of that obtained using a trajectory tracking controller alone. The proposed method achieves significant deformation suppression without degrading position tracking performance, thereby enhancing control effectiveness and system stability of flexible airships. Full article

Dual linear motor-driven systems (DLMDS) are widely used in industrial manufacturing due to their high dynamic stability and robust performance, typically featuring a symmetric Y1–Y2 axis structure. High-precision synchronization control of the motion platform is crucial for overall system performance. However, in practice, such systems are inevitably affected by mechanical installation errors, load disturbances, and nonlinear friction, which lead to the asymmetry of the Y1–Y2, severely degrading the synchronization accuracy between the two symmetric axes. To address these challenges, this paper proposes an EtherCAT-enabled active disturbance rejection control (ADRC) strategy for high-performance gantry synchronization systems. To cope with strong coupling effects, external disturbances, and high-speed operation, a master–slave synchronization architecture is developed based on ADRC and the EtherCAT cyclic synchronous torque (CST) mode. An extended state observer (ESO) is employed to estimate and compensate for lumped disturbances in real time, enabling precise synchronization without relying on an accurate mechanical model. Experimental results under both low-speed and high-speed operating conditions show that the proposed method significantly improves the synchronization stability and robustness compared with conventional cross-coupling control and master–slave control strategies. Specifically, the ADRC-based approach reduces synchronization errors by more than 20% under disturbance-free conditions and suppresses approximately 80% of disturbance-induced errors during high-speed operation. These results confirm the effectiveness and practical applicability of the proposed control strategy for high-precision gantry motion systems. Unlike conventional torque-mode implementations that merely replace the position loop with torque regulation, the proposed method introduces a disturbance-estimation-driven synchronization architecture co-designed with deterministic EtherCAT cyclic timing, which enables distributed real-time compensation beyond classical torque feedforward strategies. Full article