Search Results (196)

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

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article

Permanent Magnet Synchronous Motors (PMSMs) are central to high-performance servo drives, yet their control accuracy is often compromised by parameter uncertainties and external disturbances. While the Uncertainty and Disturbance Estimator (UDE) offers enhanced robustness by treating such uncertainties as lumped disturbances, it suffers from significant integral windup under output saturation, degrading dynamic response. This paper proposes a symmetry-principle-based UDE control strategy for the PMSM speed loop, which simplifies parameter tuning through derived analytical expressions for PI gains. To address the windup issue, two anti-windup algorithms are introduced and critically compared: a piecewise tracking back-calculation method and an integral final value prediction algorithm. The key finding is that the integral final value prediction algorithm demonstrates a superior performance. Simulation results show that it reduces the convergence time by 6.3 ms and the overshoot by 1.8% compared to the piecewise method. Experimental validation on an STM32F446-based platform confirms these findings. Under a 600 r/min step with load, the UDE controller with the integral final value prediction algorithm reduces speed overshoot by 15% compared to the piecewise algorithm and by 47% compared to the standard UDE controller without anti-windup. These results conclusively show that the proposed integrated strategy—combining symmetry-based UDE control with the integral final value prediction anti-windup algorithm—significantly improves the dynamic response, accuracy, and robustness of PMSM servo systems. Full article

The tailless flapping-wing micro aerial vehicle (FW-MAV) exhibits capabilities for hovering and agile six-degree-of-freedom flight, demonstrating potential for missions in complex environments such as forests and indoor spaces. However, limited payload and endurance restrict their practical application. This study presents a novel tailless FW-MAV named X-fly, incorporating a lightweight crank-rocker mechanism with high thrust-to-weight ratio. The optimized flapping-wing mechanism achieves a maximum single-side lift of 28.7 gf, with a lift-to-power ratio of 6.67 gf/W, outperforming conventional direct-drive propellers using the same motor. The X-fly employs servo-controlled stroke planes for tailless attitude stabilization and rapid disturbance recovery. It features a 36 cm wingspan and a net weight of 18.9 g (without battery). Using a commercially available 1100 mAh battery weighing 21.6 g, it demonstrates a peak lift-to-weight ratio of 1.42 at 3.8 V and achieves a maximum flight endurance of 33.2 min. When equipped with a 250 mAh battery weighing 5.5 g, it can carry an additional payload equal to its own net weight. The X-fly attains a maximum speed of 6 m/s and demonstrates high agility during forest flight. Furthermore, it successfully performs a simulated reconnaissance mission with an onboard camera, confirming its potential for practical applications. Full article

The servo drive system serves as the core power unit in high-end equipment such as industrial robots and computerized numerical control (CNC) machine tools, where mechanical resonance and shaft torque ripple induced by elastic deformation and backlash severely degrade motion accuracy and system stability. Conventional resonance suppression approaches, predominantly based on PI control and notch-filter-augmented PI control, suffer from critical limitations: high sensitivity to resonant frequency variations, inability to systematically enforce physical shaft torque constraints, poor robustness against parameter uncertainties and external disturbances, and significant degradation of dynamic performance when resonance is aggressively suppressed. This paper establishes a two-inertia elastic system model to investigate the effects of elastic deformation and backlash nonlinearities, revealing the mechanisms of mechanical resonance and torque ripple, and proposes control strategies for resonance suppression and shaft torque ripple limitation. A novel hierarchical control architecture is designed, consisting of a Luenberger-observer-based model predictive control (MPC) speed controller, and a super-twisting sliding mode controller (ST-SMC) for the current loop. Luenberger observer-based MPC with ST-SMC strategy is to simultaneously obtain: (a) enhanced robustness via state estimation, (b) superior dynamic performance via SMC, and (c) guaranteed shaft torque constraint satisfaction via MPC. Compared with conventional PI control and notch-filter-based PI control, simulation results demonstrate that Luenberger observer-based MPC with ST-SMC strategy effectively suppresses resonance, limits shaft torque ripple, and enhances the system’s disturbance rejection capability. Full article

Electro-hydraulic servo pump-controlled systems have advantages such as energy saving and high integration and are widely applied in aerospace, engineering machinery, and other fields. However, the input and state delays introduced by drive circuit, control period, and oil leakage result in lower dynamic response speed compared to traditional valve control systems, which restricts the promotion of the system. In this paper, a prescribed performance trajectory tracking control method is proposed to improve the transient and steady-state performance of the system. A performance function is designed to constrain the range of trajectory tracking errors. The constrained space is mapped to an unconstrained space via a homeomorphic transformation, and the control laws are designed by integrating it with the backstepping method. In the final step of the backstepping design, the input and state delays are processed using Lyapunov–Krasovskii functionals. The simulation and experimental results show that under the condition of fixed input delay and state delay, the trajectory tracking errors converge within the preset range, and all states of the system are uniformly bounded. The results demonstrate the effectiveness of the proposed method in this paper. Full article

Thepaper presents an efficient model predictive control framework formulated as a linear programming problem to control a servo drive with model uncertainty considerations from the viewpoint of the control performance. The model predictive framework is used to adopt $L_1$-type cost functions using absolute tracking errors, providing computational efficiency and enabling real-time implementation. A key contribution is the deployment of this approach on real hardware in a hardware-in-the-loop setting, supported by fully open-source code for Simulink Coder and C environments, verifying the solution scheme in real time. Experimental validation on a servo drive demonstrates the system’s tolerance for parameter uncertainties with slight performance degradation, resulting in an up to 18% increase in the considered control quality measure, between nominal parameters’ values and the worst configuration. The proposed linear programming approach enables constraint handling imposed on control signals and supports the arbitrary choice of prediction horizons and sampling intervals. The paper also includes a comprehensive derivation of the control law, controller implementation details, and stepwise experimental results showcasing the impact of uncertainties on control performance. This work and the attached code enable the authors to easily reproduce the proposed approach and extend it in their applications. Full article

The electric spindle serves as a critical component in enabling a highly dynamic response, stable torque output, and precise motion control for the main cutting operations of CNC machine tools. The design precision and control performance of its drive motor directly influence the geometric accuracy, surface quality, and overall machining efficiency of the workpiece, thereby determining the comprehensive performance of advanced CNC systems. This paper begins with a systematic review of the global industrial layout of CNC machine tool and electric spindle manufacturers, highlighting regional clustering patterns and technological development trends across key manufacturing regions. Subsequently, it classifies and elaborates on the differentiated technical requirements for the electric spindle motor in terms of wide-speed-range servo capability, high-efficiency operation, adaptability to high-speed and high-power cutting loads, and precision maintenance under high-speed conditions, based on the process characteristics of different types of CNC machine tools. A comprehensive overview of the current state of research is provided with respect to electric spindle motor design and control technologies. Finally, forward-looking perspectives are presented on future development directions, particularly in the areas of multi-physics coupling co-design and the integration of intelligent control algorithms, aiming to offer a solid theoretical foundation and strategic guidance for the advancement and engineering application of high-performance electric spindles. Full article

This work presents and validates an eye-tracking-based visual system for driving the delta robot. A delta robot is tracked by image processing based on vision servo control. The vision servo program is developed in C++ to perform image processing-based object detection. For image processing, Haar classifier-based methods are used. Finally, image processing and motion controller movements are integrated into one system to perform the visual servo-based motion of the end effector of the delta robot. Experiments are performed to validate the proposed method from the perspective of image processing. Moreover, this paper validates the kinematic analysis, which is vital for obtaining 3D information on the end-effector of the delta robot. The presented model can be implemented in eye clinics to facilitate ophthalmologists by replacing manual eye-checking equipment with automatic, unattended, computerized eye checkups. Full article

This paper presents a genetic algorithm (GA) approach to optimize key structural parameters of the torque motor used in a direct-drive slide knife gate valve. The optimization aims at enhancing the performance of the torque motor by improving the output torque, minimizing the overshoot, and reducing the response time. A mathematical model based on these performance indicators is formulated to guide the optimization process. Compared to the original design, the optimized design is shown to achieve a 26.4% increase in output torque, a 0.14 ms reduction in response time, and a 9% decrease in overshoot. Additionally, AMESim simulations confirm that the optimized motor significantly improves valve control accuracy, dynamic response, and flow stability, while also decreasing sensitivity to pressure fluctuations under high-current conditions. Finally, experimental results are provided to corroborate the simulation findings, validating the accuracy and effectiveness of the proposed optimization methodology. This study provides novel theoretical insights and practical guidance for the design of high-performance torque motors used in direct-drive electro-hydraulic servo valves within aerospace applications. Full article