Search Results (294)

Hydraulic manipulators face critical challenges due to valve dead-band nonlinearity and state constraints, which can lead to safety hazards and hardware damage. This study proposes a state-constrained controller with valve dead-band compensation to ensure prescribed positioning accuracy and operational safety. Barrier Lyapunov functions ensure that state constraints are maintained and that boundary violations are avoided. Concurrently, a smooth dead-band inverse model is developed to offset asymmetric valve dead-band effects without inducing chatter. Adaptive laws estimate uncertain parameters and dead-band impact in real time, and a disturbance observer attenuates unmatched uncertainties. Dynamic surface control is employed to diminish the explosion of complexity in backstepping design. Comparative simulations under fixed-angle and arbitrary-angle tracking demonstrate that the proposed controller achieves superior tracking accuracy with steady-state errors below 0.04° compared to 0.06° for non-compensated controllers, while significantly reducing pressure fluctuations and control chattering as adaptive parameters converge. The results indicate that the strategy effectively compensates for valve dead zones while strictly maintaining state constraints, thereby achieving the required control precision for hydraulic servo systems. Full article

The ability to reliably press physical buttons is a common requirement in robotics. Conventional vision-based methods often suffer from positional errors during execution if the end-effector’s approach is not perpendicular to the target surface. This paper proposes a novel dual-quaternion-based visual servoing method that enables robots to reach desired poses and enhances accuracy in robotic button-pressing. Our method acquires target pose information (position, depth and surface normal direction) from the RGB-D camera and converts it into dual quaternion representation to construct the visual servoing control system. The image Jacobian matrix for the dual quaternion pose is then computed. The dual-quaternion-based visual servoing ensures that the pressing direction and the optical axis of the coaxially mounted camera remain perpendicular throughout the pressing motion, thereby eliminating misalignment between the actual contact point and the visually identified target. By representing spatial displacements in SE(3) with dual quaternions, our method enables more compact, concise, and efficient pose representation and computation throughout the visual servoing process. Experimental results demonstrate that, compared to conventional methods, our technique achieves more efficient visual servoing control, significantly improving both positioning accuracy and computational efficiency. 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

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%. Full article

Electro-hydraulic servo systems (EHSS) are widely used in industrial applications due to their high power-to-weight ratio; however, their nonlinear dynamics pose significant challenges for precise position control. This study proposes and validates a real-time Proportional–Integral–Derivative (PID) control system implemented on a Field Programmable Gate Array (FPGA) platform for the angular positioning of a servo-hydraulic actuator. The control algorithm is deployed on an embedded system to achieve high-speed execution independent of host processing. The controller gains were tuned using system identification techniques based on step response analysis. The system’s performance was experimentally assessed under both step inputs and sinusoidal trajectories. Experimental results demonstrated that the proposed controller achieved a rise time of 0.06 s and a steady-state error within ±1° for small step inputs. Furthermore, frequency domain analysis via Bode diagrams validated the system’s dynamic bandwidth, showing exceptional tracking capabilities at 10 Hz excitation with a negligible phase lag of −0.71°. These findings confirm that an FPGA-based PID control architecture effectively overcomes hydraulic nonlinearities, providing a robust and precise solution for real-time motion control compared to traditional methods. Full article

Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) struggle to balance end effector pose accuracy and robustness in apple picking. They are also prone to target loss and control singularities. A progressive Hybrid Automatic Switching Visual Servoing (HAVS) method is proposed and applied to an apple-picking robotic system. HAVS integrates PBVS and IBVS to coordinate control of the manipulator end effector pose. A depth-based switching function is designed. When target depth is below an optimal threshold, the controller switches to PBVS for precise final positioning. This reduces target loss and control singularities. An adaptive proportional-derivative (PD) controller with fuzzy gain scheduling updates the control gains online to enhance responsiveness and stability. The hardware consists of a six-axis manipulator, a depth camera, and a mobile base. You Only Look Once version 5 (YOLOv5) performs apple detection and generates control commands. Indoors, success rate was 96%, which was 4 and 10 percentage points higher than PBVS only and IBVS only. Average picking time was 12.5 s, 0.3 s, and 1.1 s shorter. Outdoors, success rate was 87.5%, average time was 13.2 s, and damage rate was 4.2%. This method provides a reference implementation for visual servo control in agricultural picking robots. Full article

The Electro-Hydraulic Servo Actuator for Active Suspensions (ASEHSA) plays a decisive role in shaping the holistic performance of vehicle suspension systems through its dynamic response speed and control precision. However, achieving high-performance control of ASEHSA still faces challenges. On one hand, existing model-based control methods are highly sensitive to parameter uncertainties and unmodeled nonlinear hydraulic dynamics, which can easily lead to reduced robustness in practical applications. On the other hand, traditional model-free strategies have limited time-delay compensation capabilities and often struggle to balance overshoot and settling time under delayed and disturbed conditions. To resolve this challenge, this study proposes an improved model-free adaptive control method that incorporates the differentiation of the tracking error (DE-IMFAC). Within the framework of traditional model-free adaptive control (MFAC), this approach reconfigures the time-delay term from an explicit form in the control law to implicit management, substantially mitigating the influence of time delays on system control performance. At the same time, by refining the performance criterion function and integrating a tracking error differentiation term together with dynamic weighting factors, the dynamic performance and adjustment flexibility of the controller are significantly enhanced. Additionally, by leveraging the characteristic equation of discrete autonomous systems and compression mapping theory, the BIBO stability of the DE-IMFAC control system and the monotonic convergence of the tracking error are rigorously established through theoretical analysis. Simulation and experimental results demonstrate that, compared with PID and traditional MFAC methods, DE-IMFAC significantly reduces integral absolute error, overshoot, settling time, and maximum position tracking error, while improving disturbance rejection capability. This approach does not depend on an accurate mathematical model of the ASEHSA system and maintains robust dynamic performance under complex operating environments characterized by time delays and data disturbances, providing a practical solution for ASEHSA and related industrial control systems. Full article

Interactive teleoperation offers an intuitive pathway for human–robot interaction, yet many existing systems rely on dedicated sensors or wearable devices, limiting accessibility and scalability. This paper presents a vision-based teleoperation framework that enables real-time control of an articulated robotic arm (five joints plus a gripper actuator) using human hand tracking from a single, typical laptop camera. Hand pose and gesture information are extracted using a real-time landmark estimation pipeline, and a set of compact kinematic descriptors—palm position, apparent hand scale, wrist rotation, hand pitch, and pinch gesture—are mapped to robotic joint commands through a calibration-based control strategy. Commands are transmitted over a lightweight network interface to an embedded controller that executes synchronized servo actuation. To enhance stability and usability, temporal smoothing and rate-limited updates are employed to mitigate jitter while preserving responsiveness. In a human-in-the-loop evaluation with 42 participants, the system achieved an 88% success rate (37/42), with a completion time of 53.48 ± 18.51 s, a placement error of 6.73 ± 3.11 cm for successful trials (n = 37), and an ease-of-use score of 2.67 ± 1.20 on a 1–5 scale. Results indicate that the proposed approach enables feasible interactive teleoperation without specialized hardware, supporting its potential as a low-cost platform for robotic manipulation, education, and rapid prototyping. Full article

A wearable elbow exosuit system has been proposed in this work, including the origami-inspired exosuit structure along with a portable air source and electromyography (EMG)-based active rehabilitation control method. The elbow exosuit is designed using an origami-inspired pneumatic actuator to meet the biomechanic requirements for elbow assistance. And a portable pneumatic source attached to the waist is also proposed to drive the elbow exosuit. On the basis of exosuit structure design, the active control with cascaded frame is then developed. For the active perspective, the EMG-based motion prediction is accomplished for the input of controller. To achieve real-time and accurate prediction, a simple feedforward neural network is utilized for a motion prediction model based on its fast training. To further reduce the size of the network, the features are extracted from the EMG and angle for the inputs, replacing the end-to-end method. Based on intention prediction, the cascaded controller subsequently completes position control, torque control and pressure servo control. Finally, through preliminary experiments on healthy participants, the elbow can be accurately predicted for the EMG-based method, and the assistance efficiency is verified through task scores and reduction in muscle activation. In summary, the proposed wearable exosuit can provide a reference for the design of wearable devices. Full article

Aiming at the problem that the position control accuracy of the traditional semi-closed-loop control and the vibration caused by the nonlinear characteristics of the system are easily affected by the full closed-loop control, a double-loop position feedback control based on the state information feedback of the motor and the load is proposed. Based on the double-loop position feedback control framework, a vibration suppression method combining the linear extended state observer, torque feedback compensation and speed feedforward is introduced. The simulation results show that the proposed control method effectively suppresses load vibration, improves the system’s servo control performance, and maintains position control accuracy. Full article

The existence of friction, flow–pressure coupling, load variations, internal leakage, and other fluidic nonlinearities makes it challenging to design classical model-based controllers for servo-valve-driven electro-hydraulic actuators (EHAs). To address these issues and achieve high-precision output tracking, this paper proposes a learning-based control framework that integrates Long Short-Term Memory with Deep Q-Learning and Bayesian Optimization (BO–LSTM–DQN) for high-precision position regulation of servo-valve-driven EHAs. In this framework, the LSTM augments Q-learning with temporal memory to first establish and infer hidden dynamics from measured sequences. Meanwhile, Bayesian Optimization is used to automatically optimize key hyperparameters to improve convergence and policy stability, without requiring manual trial-and-error. Additionally, a constraint-aware reward function is formulated to encode realistic servo-valve operational limits and satisfy motion stability requirements. The effectiveness of the proposed control strategy is verified through comparative simulations with PID– and BO–DQN-based controllers under different operating scenarios, subject to load disturbance and internal leakage. Furthermore, to evaluate the robustness of the proposed controller against parametric uncertainties, extensive Monte Carlo simulations are conducted with simultaneous variations of up to $50\%$ in five key system parameters. The results demonstrate that the proposed BO–LSTM–DQN framework achieves a significant reduction in Root Mean Square Error (RMSE) by up to $51.79\%$ compared with the conventional PID and maintains superior stability over the optimized DQN baselines, confirming its effectiveness for real-world EHA applications under extreme operating conditions. Full article

Legged robots inspired by animal locomotion require actuators with high power density, fast response, and robust force control, yet traditional valve-controlled hydraulic systems suffer from substantial energy losses and weak regeneration performance. Motivated by role allocation across gait phases in animal legs, where in-air positioning requires far less actuation effort than ground contact support and force modulation, this work proposes a novel gas–oil hybrid servo actuator, denoted GOhsa, for quadruped knee joints. GOhsa utilizes pre-charged high-pressure gas to pressurize hydraulic oil, converting the conventional dual-chamber pressure servo control into a single-chamber configuration while preserving the original piston stroke. This architecture enables bidirectional position–force control, enhances energy regeneration applicability, and improves operational efficiency. Theoretical modeling is conducted to analyze hydraulic stiffness and frequency-response characteristics, and a linearization-based force controller with dynamic compensation is developed to handle system nonlinearities. Experimental validation on a single-leg platform demonstrates significant energy-saving performance: under no-load conditions (simulating the swing phase), GOhsa achieves a maximum power reduction of 79.1%, with average reductions of 15.2% and 11.5% at inflation pressures of 3 MPa and 4 MPa, respectively. Under loaded conditions (simulating the stance phase), the maximum reduction reaches 28.0%, with average savings of 10.0% and 9.8%. Tracking accuracy is comparable to traditional actuators, with reduced maximum errors (13.7 mm/16.5 mm at 3 MPa; 15.0 mm/17.8 mm at 4 MPa) relative to the 16.6 mm and 18.1 mm errors of the conventional system, confirming improved motion stability under load. These results verify that GOhsa provides high control performance with markedly enhanced energy efficiency. Full article

A target state estimation method based on multiple quadrotors is proposed for unknown maneuvering targets, and a distributed formation control method Image-Based Visual Servoing (IBVS) is also proposed to achieve encirclement tracking of unknown maneuvering targets. In the tracking control, collision avoidance constraints for nodes within the formation are also introduced, and based on the shared position information within the formation, the positions of other nodes within the Field of View (FOV) of each node are predicted for detecting unknown targets. Firstly, an Interacting Multiple Model (IMM) was designed based on multiple motion modes to estimate the position and velocity of the target. A virtual camera coordinate system containing translational and yaw rotations was established between the formation and the target based on the estimated values. Then, a distributed control method based on IBVS was further designed by combining image deviation. At the same time, a safe distance between nodes within the formation was introduced, and collision avoidance constraints of the Control Barrier Function (CBF) were designed. Finally, the position of the formation nodes within the FOV was predicted. The simulation results demonstrate that, utilizing the proposed estimation method, the estimation accuracy for target velocity improves by 26.5% in terms of Root Mean Square Error (RMSE) compared to existing methods. Furthermore, the proposed control method enables quadrotor formations to successfully achieve encirclement tracking of unknown maneuvering targets, significantly reducing tracking errors in comparison to conventional approaches. Full article

The dynamic response accuracy of suspension servo actuators directly determines the vibration-reduction performance of active-suspension systems. However, during long-term service, the system is prone to the influence of compound-degradation faults, such as internal leakage and time delay, leading to a significant decline in control performance. To address this issue, this paper proposes a collaborative control framework combining model-free adaptive control with a differential term of tracking error (DE-MFAC) and an improved particle swarm optimization (IPSO) algorithm. Firstly, to overcome the limitations of traditional model-free adaptive control (MFAC), a DE-MFAC strategy is constructed by implicitly handling the time-delay term and introducing the differential term of tracking error and dynamic weight factor into the performance index. Secondly, to enhance the parameter-tuning effect, the traditional particle swarm optimization (PSO) algorithm is improved (IPSO) by incorporating a dynamic inertia weight and an out-of-bounds random reflection mechanism, thereby strengthening the global optimization capability. On this basis, a suspension servo actuator system model incorporating internal leakage and time-delay faults is established based on the co-simulation platform of Simulink and AMESim, and the proposed method is validated. The simulation results show that, compared with the optimized traditional MFAC, the DE-MFAC tuned by IPSO exhibits superior position-tracking accuracy, faster response speed, and stronger overshoot-suppression capability under various compound-fault conditions. Further analysis indicates that the Integral of Absolute Cubic Error (IACE) function, due to its higher sensitivity to large deviations, can more effectively suppress overshoot and is suitable for engineering scenarios with strict requirements on dynamic performance. In addition, the optimization of control parameters using the IPSO algorithm can effectively compensate for the performance degradation caused by degradation faults, providing a feasible technical approach for extending the service life of actuators through adaptive adjustment. 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