Search Results (206)

Periodic and non-periodic disturbances significantly affect the tracking accuracy of servo systems. A dual-motor drive composite control strategy based on iterative learning feedforward control and super-twisting sliding mode observer is proposed. Initially, a novel reaching law capable of dynamically adjusting gain coefficients based on system states is introduced, leading to the design of a sliding mode controller with proven asymptotic stability. To address non-periodic total disturbances, a super-twisting sliding mode observer is developed, and Lyapunov stability theory is employed to demonstrate system stability and error convergence to zero. A resonant controller is designed to suppress medium- to high-frequency periodic disturbances. For periodic total disturbances, a parameterized feedforward controller based on iterative learning is devised, and an input-shaping filter is introduced to refine the input trajectory. The feedforward control parameters are iteratively updated using a data-driven approach. Experiments are conducted on a differential dual-drive servo system. The nut motor adopts the sliding mode controller with an observer. The screw motor employs the iterative learning feedforward controller. Results show effective suppression of the disturbances. Speed ripple is reduced, and tracking accuracy is significantly improved. The study demonstrates the feasibility and advantage of combining robust control with iterative learning in high-precision servo systems. Full article

To address the complex dynamic behavior of four-axis precision motion platforms under high-speed and high-acceleration conditions, as well as the difficulty of traditional modeling methods in balancing accuracy and efficiency, this paper proposes a data/model-driven dynamic modeling and analysis method that integrates generalized receptance coupling substructure analysis (GRCSA) with artificial intelligence (AI) algorithms. Based on the GRCSA theory, the initial analytical framework of the dynamic model of the precision motion platform is established, and the frequency response functions (FRFs) of the substructure and interface are preliminarily obtained. On this basis, the nonlinear prediction model of the dynamic parameters of the interface driving direction is established by using the AI algorithm, enabling fast and accurate prediction of the dynamic characteristics of the interface under different servo control parameters in the guide rail driving direction. Finally, based on the data/model-driven dynamic modeling and analysis method, the interface control parameters are optimized. The interface and substructure parameters are modified to reduce the prediction error of the FRFs from 3.50% to 2.47%. This method can achieve the prediction error of the dynamic characteristics of the interface under different control parameters of about 2.5%. Full article

Gantry-type dual-drive feed systems are widely used in high-precision CNC machine tools, and their synchronization performance directly affects machining accuracy and operational stability. To reduce synchronization errors caused by load-position variation, nonuniform stiffness distribution, and inertia mismatch, this study proposes a structural parameter optimization method for a gantry-type dual-drive feed system. The novelty of this work lies in integrating position-dependent dynamic modeling, critical-position identification, sensitive structural-parameter selection, and response-surface-based optimization into a unified framework for synchronization-error reduction. First, a position-dependent dynamic model is established using modal reduction, spline interpolation, and substructure synthesis. The dynamic model is then coupled with a servo control model to construct an electromechanical coupling model, which is validated experimentally on a gantry-type dual-drive feed system. Next, the synchronization-error distribution over the entire workspace is evaluated, and the critical position with the poorest synchronization performance is identified. Based on sensitivity analysis, the key structural parameters affecting synchronization error are selected as design variables. A response surface surrogate model is then constructed, and particle swarm optimization is used to obtain the optimal structural-parameter combination. The results show that the synchronization error at the critical position is reduced by 20.5%, while the average synchronization error at the validation positions is reduced by 17.3%. These results demonstrate that the proposed method can effectively improve the synchronization accuracy of gantry-type dual-drive feed systems and provide practical guidance for the structural design of high-precision dual-drive machine tools. Full article

The higher the rotational speed of the telescope dome, the greater the vibration and noise are induced, which results in a more significant impact on telescope imaging performance, while also requiring greater driving power and increasing the control complexity. Therefore, this paper primarily focuses on appropriately reducing the dome speed during high-speed space target tracking without affecting observation effectiveness. First, the initial tolerance of the dome opening in the telescope’s horizontal state is introduced, and the variation pattern of the initial tolerance with the telescope’s elevation angle is derived; then, the angular velocity relationship between the dome and the telescope is established, and the rotational trajectory of the dome is replanned. Taking the International Space Station as an example for simulation, the results show that the maximum velocity of the dome is reduced by 25.4% compared with that of the telescope, with no field-of-view obscuration during the entire observation process. Finally, a multi-motor servo control system for the dome is designed, and practical tests demonstrate that during synchronous tracking with the telescope, the synchronization error PV of all motors is less than 2.5%, the dome tracking accuracy is better than 60″, and the maximum dome speed is reduced by approximately 33.3% compared with the telescope. This research is of great significance for appropriately reducing the dome speed requirement, alleviating high-speed vibration and noise, and simplifying control difficulty in high-speed tracking. Full article

The continuous rotating valve plate piston pump (CRVPPP) can efficiently drive actuators such as hydraulic cylinders or hydraulic motors to generate excitation motion. This CRVPPP-driven excitation system can avoid the throttling losses associated with servo-valve-controlled excitation systems. However, this excitation system exhibits an asymmetric excitation phenomenon during actual operation. Through theoretical analysis and experimental research on the mechanical characteristics of the valve plate pair in the CRVPPP, it was found that the asymmetric excitation originates from the annular grooves of the fixed valve plate alternating between oil suction and discharge states. This alternation subjects the rotating valve plate to an overturning moment, which in turn causes a periodic variation in the end-face clearance of the valve plate. Targeting the asymmetric and nonlinear leakage characteristics of the CRVPPP, an adaptive neural network module was established based on the Amesim-Matlab/Simulink co-simulation framework. This module incorporates the mapping from the rotational speeds of the rotating valve plate and cylinder block to the equivalent leakage opening of the distribution grooves. By training with experimental data, the CRVPPP- driven excitation system model was formulated. Experimental results show that the established model achieves a correlation coefficient of 0.99786 on the training set, indicating its excellent fitting accuracy. Furthermore, the mean squared error on the test set is within 0.04 mm², demonstrating the model’s good generalization ability. It can reproduce the dynamic characteristics of the CRVPPP-driven excitation system with high precision, thereby laying a solid modeling foundation for the characteristic analysis, structural optimization, and high-precision control of such excitation systems. Full article

A unique motor–syringe air control system is introduced to power a PneuNet-inspired silicone soft robotic hand. The system consists of a novel motor–syringe air drive (MSAD) pneumatic device with a crank–slider mechanism that integrates the functionalities of common medical syringes and servo motors. This novel system is integrated with pressure and flex sensors to overcome challenges encountered with using traditional compressor-powered actuation systems to achieve superior linear pressure profiles, provide precise control over soft finger movements, and minimize the noise emitted. Our soft finger design is inspired by the architecture of PneuNet pneumatic actuators and is further optimized by performing ANSYS Workbench (version 2023) simulations to considerably enhance the bending efficiency. A pressure sensor is deployed in each finger chamber for the purpose of grasping force control in terms of air pressure. Furthermore, the deployed pressure sensor in each finger chamber can also continuously monitor the air leakage, and a replenishment valve can be activated when the air leakage significantly affects (i.e., less than 95% of target pressure) the actuation to restore atmospheric pressure in the syringe chamber to restore the MSAD function. Different tests on bending and grasping (including grasping objects with various shapes) are performed to verify the performance of the proposed pneumatic actuator and the five soft fingers. Full article

Rotary direct-drive servo valves (RDDSVs) have gained significant attention in high-performance electro-hydraulic servo systems due to their compact structure, rapid dynamic response, and high power density. However, improving the transient performance and steady-state accuracy of RDDSVs remains a challenge, primarily owing to inherent strong nonlinearities and disturbances characterized by high-frequency fluctuations and unmodeled uncertainties. To address these issues, this paper proposes an intelligent state-constrained control strategy with neural network-based real-time compensation for RDDSVs. Specifically, a nonlinear constraint function is introduced to directly restrict the range of state variables, thereby enhancing the system’s transient response. Subsequently, the universal approximation property of adaptive neural networks is exploited to estimate unmodeled disturbances, which significantly improves steady-state precision. Furthermore, nonlinear filtering technology is integrated to mitigate the computational burden on the controller while enhancing overall robustness. The stability of the closed-loop system is rigorously proven using Lyapunov theory. Finally, comparative simulations are carefully conducted to apply different control algorithms. The results validate the effectiveness and superiority of the proposed control algorithm. Full article

Autonomous driving in underground mines faces significant challenges due to Global Navigation Satellite System (GNSS) denial and harsh environmental conditions. Mainstream multi-sensor fusion and Simultaneous Localization and Mapping (SLAM) schemes have achieved substantial progress in underground navigation, but their deployment in feature-sparse tunnels may still face challenges related to computational burden and perception robustness. This study explores an infrastructure-assisted navigation architecture that transforms the roadway into a structured luminous guidance channel by deploying programmable Light Emitting Diode (LED) strips along the tunnel roof. The proposed system simplifies complex three-dimensional pose estimation into a two-dimensional visual servoing task targeting optical signals. Central to this approach is a robust data fusion strategy that utilizes a topology matching algorithm to map noisy Ultra-Wide-band (UWB) coordinates onto a discrete LED index space, thereby providing a reliable global positioning reference. Furthermore, a hierarchical fault-tolerant controller based on a Finite State Machine (FSM) is designed to facilitate seamless degradation to a UWB-assisted ultrasonic wall-following mode in the event of visual degradation, supporting fault-tolerant operation under controlled laboratory conditions. Experimental results in a laboratory simulation environment demonstrate that the system achieves millimeter-level static initialization accuracy, a dynamic tracking Root Mean Square Error of approximately 4 cm, and a 100% autonomous recovery rate from visual failures in straight tunnels. These results demonstrate the feasibility of the proposed infrastructure-assisted route under controlled laboratory conditions and suggest its potential as an engineering reference for structured underground transport scenarios with acceptable infrastructure modification. Full article

To address the issues of poor operational stability and insufficient omnidirectional leveling capability of tracked electric agricultural machinery in hilly and mountainous areas, this paper presents an electromechanical omnidirectional leveling chassis architecture based on a dual-layer independently driven architecture. Utilizing servo electric cylinders as actuators, a leveling mechanism with physically decoupled upper lateral and lower longitudinal layers was constructed. Based on this structure, a mathematical model relating the electric cylinder displacement to the platform posture was established. Furthermore, an ADAMS dynamics simulation platform was built to conduct simulation analysis and prototype experiments. The results indicate that the designed dual-layer independently driven chassis can achieve a theoretical leveling range of ±28.6° laterally and ±27.7° longitudinally, operating smoothly under the rated 25° slope condition. Dynamic tests demonstrate that when the prototype travels at 3 km/h, the residual inclination angle of the platform can be controlled within ±0.9° in 3 s. The simulation and experimental results are in high agreement, comprehensively revealing the dynamic coupling relationship among the electric cylinder displacement, platform posture, and driving thrust. The experiments verify that the electromechanical omnidirectional leveling system can accomplish adaptive leveling under slope conditions, exhibiting superior performance regarding response speed, control accuracy, and disturbance rejection, with the thrust deviation rate between simulation and experiment within 6.71%. Full article

Servo electric cylinders have been widely adopted in high-performance linear drive applications such as aerospace systems, robotic servo systems, medical equipment, advanced manufacturing, precision testing, and high-end equipment due to their advantages, including high cleanliness, compact structure, high transmission efficiency, and ease of achieving precise control. However, under complex operating conditions, system performance is influenced not only by control strategies but also closely related to factors such as friction, clearance, transmission flexibility, structural vibrations, and modeling accuracy. This paper reviews mainstream control strategies and dynamic simulation methods for servo electric cylinders, providing structured analysis and systematic evaluation of representative research. In terms of control strategies, key approaches, including classical PID control, robust nonlinear control, intelligent and learning-based control, and active disturbance rejection control, are discussed, with comparative analysis of their characteristics and limitations in tracking accuracy, robustness, adaptability, and engineering feasibility. Regarding dynamic modeling and simulation, methods such as multibody dynamics, finite element analysis, rigid-flexible coupling, and multi-domain collaborative simulation are reviewed, examining their applicability in nonlinear mechanism characterization, local structural response assessment, and high-fidelity system modeling. Current research indicates that servo cylinder control is evolving from single-method improvements toward integrated and composite approaches, while dynamic modeling has progressed from low-order simplified analyses to system-level, multi-level, and high-fidelity descriptions. Existing studies still face challenges, including insufficient unified evaluation criteria, inadequate cross-method comparisons, and insufficient integration between control design and high-fidelity models. Future research should focus on enhancing control-model co-design, experimental validation under complex conditions, and system-level optimization oriented toward intelligent and high-reliability systems. Full article

A novel hydraulic circuit, the Modular Hydraulic Servo Booster (MHSB) is applied to redundant hydraulic manipulators. The MHSB uses multiple pumps and valves to drive multiple actuators to significantly improve energy efficiency compared with conventional servo-valve systems. Our previous work has proposed a control strategy that incorporates energy-optimal trajectory planning and operation mode switching using a graph search algorithm to perform point-to-point (PTP) tasks for manipulators. This paper extends our previous study by constructing an optimal-posture table that incorporates manipulability. By using this table to evaluate the cost in graph search, we achieve real-time optimal trajectory planning and operation mode switching for redundant manipulators. Numerical simulation from different PTP tasks on a three-link manipulator (1-m length, 10-kg weight) validate the proposed method. Full article

One of the key factors determining energy consumption and control stability in hydraulic servovalves with direct electric drive is the flow forces acting on the spool. These forces are complex in nature and consist of both steady-state and transient components, with the steady-state component exerting the dominant influence on the performance and dynamics of spool valves. In recent years, this issue has become the subject of intensive research aimed at reducing undesirable hydraulic loads while maintaining high nominal flow capacity, strong energy efficiency, and low manufacturing cost. In engineering practice, the most effective approach has proven to be the modification of the spool geometry in order to control the direction and jet angle of the outflow while keeping the valve sleeve design as simple as possible. This solution reduces the forces acting on the spool without the need to redesign the flow channels or increase production complexity. This study presents classical analytical methods used to calculate flow forces in typical spool valve designs, which serve as a reference point for subsequent investigations. Then, using CFD simulation tools, a method of flow force compensation is demonstrated for selected spool geometries, followed by a detailed comparative analysis of their effectiveness. The results may provide a foundation for developing more energy-efficient and dynamically stable direct-drive servovalve constructions. Full article

Time delays are inherent in modern motion-control and electric-drive loops due to sensing, filtering, sampling and computation, communication, and actuation scheduling. When such delays are only partially known, they can markedly reduce stability margins and narrow the admissible range of state-feedback gains, especially in high-bandwidth servo applications. This paper develops a design-oriented state-feedback framework for delay-affected plants based on the Manabe polynomial concept and the Coefficient Diagram Method (CDM). The plant is represented as a chain of integrators of order two to four with an effective input gain, and the feedback gain is synthesized for the nominal delay-free model by matching a standard Manabe/CDM characteristic polynomial using the classical CDM stability-index pattern. When an unmodeled input delay is present, the closed loop is governed by a delay-dependent characteristic equation. By introducing a normalized representation, the analysis yields explicit delay-stability limits that directly translate into a lower bound on the equivalent time constant used for tuning. The degradation of the phase margin and gain margin with increasing normalized delay is quantified as design charts, and a simple phase-margin-based inequality is proposed for selecting the tuning time constant, with gain-margin checks recommended as a verification step. Full article

This paper presents the design of a high-efficiency apple harvesting robot based on a hybrid pneumatic-electric drive system, capable of operating around the clock. The robotic system comprises a mobile platform with two degrees of freedom (DOF) and a five-DOF PRRRP manipulator for fruit picking. To meet the harvesting requirements, a spoon-shaped end-effector with pneumatic control was developed, enabling precise manipulator control and flexible grasping. The robot’s vision system integrates machine vision and deep neural network approaches. Additionally, an industrial computer and AC servo drivers were employed to control the manipulator and end-effector. An integrated nighttime illumination system allowed for all-weather operation. Initial experiments were conducted in a controlled laboratory. Subsequently, comprehensive identification and harvesting tests were performed in both laboratory and field environments to validate system robustness. Experimental results validated the effectiveness of the proposed system, demonstrating an apple harvesting success rate of 81% and an average harvesting time of 7.81 s per apple. The system achieved a fruit damage rate of less than 5% during field experiments, demonstrating its potential for gentle handling. The primary innovation of this work lies in its hybrid drive architecture and adaptive vision strategy, which together offer a cost-effective and robust solution for all-weather automated harvesting, addressing key limitations of high cost and environmental sensitivity in existing robotic harvesters. 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