Search Results (120)

The electro-hydraulic track tensioning system of a tracked vehicle directly affects track engagement stability, vibration response, and energy utilization efficiency under complex terrain and time-varying loads. Accurate and robust control is therefore of great engineering significance. This paper focuses on an electro-hydraulic tensioning system with a composite actuation structure consisting of a proportional main valve and two 2/2 on–off valves and proposes a learning-enhanced nonlinear model predictive control (L-NMPC) method. Residual learning, adaptive weight/constraint scheduling, and execution-layer mode coordination are integrated into a unified predictive control framework. The study is carried out on a strongly coupled Simulink–AMESim–RecurDyn co-simulation model and an LF1352 prototype-vehicle test platform. Comparative evaluations are conducted under steady step-and-ramp tracking, random rough terrain, sudden steering/braking pulses, supply-pressure limitation, and parameter drift/sudden-change conditions. The evaluation indices include track-tension tracking error, peak overshoot, settling time, energy consumption, and stability under parameter mismatch. Compared with conventional nonlinear model predictive control (NMPC), the proposed L-NMPC reduces the root-mean-square error of track tension by 42–58%, decreases peak overshoot by 30–40%, shortens settling time by 25–35%, and achieves a 12–17% reduction in energy consumption at the simulation level. Under ±20% parameter perturbation, the fluctuation in track tension can be constrained within ±1.1 kN. The simulation and real-vehicle results remain consistent in terms of the dominant dynamic trends and performance ranking. This study provides a verifiable implementation path for model–data-fusion control of strongly coupled electro-hydraulic actuation systems and offers an engineering reference for intelligent, energy-efficient, and highly reliable control of tracked-vehicle chassis systems. 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

Energy efficiency and sustainability are core issues in the modern design and management of industrial machinery and plants. These concerns are reflected and reinforced by the Sustainable Development Goal 9 of the United Nations (SDG9), “Industry, innovation and infrastructure”, which enshrines efficiency and optimized energy use as key features of sustainable production systems. As the engineering of industrial machinery reorients itself towards energy sustainability, attention is naturally shifting to actuators, since these components unavoidably waste part of the considerable amount of energy they absorb to execute their functions. Hydraulic actuation systems, while uniquely suited to heavy-duty applications, are particularly affected by poor energy conversion efficiency, in part due to their intrinsic properties but also because of outdated yet still common industrial practices. Consequently, for this actuation technology, there are wide margins for improvement in terms of energy waste reduction and increased environmental sustainability. This paper, therefore, investigates new applications for a management and control method conceived by the authors to drastically and systematically reduce the energy consumption of hydraulic actuators. The method is easily retrofittable to existing plants, being based on the unconventional and non-invasive deployment of a continuous-control electrohydraulic valve (CCEV) to control the supply pressure, whose required value is estimated according to the instantaneous load demands. Through the simulation of several industrial processes characterized by process parameters of varying orders of magnitude, this paper demonstrates that this innovative use of a CCEV for supply pressure regulation is an effective and widely applicable solution for energy savings and CO₂ footprint reduction in production systems that rely on hydraulic servo axes. Full article

Due to the complex operating environment of tracked vehicles, experimental braking tests using real vehicles are typically costly and time-consuming. Furthermore, limitations in testing environments make it difficult to comprehensively evaluate a system’s braking performance across diverse operating scenarios. To overcome these limitations, this paper proposes the construction of a high-precision digital model to simulate the real braking process of tracked vehicles in a virtual environment and validates the model through experiments. The results show that braking pressure changes continuously and proportionally with the pedal angle, the system response time is less than 0.3 s, braking pressure builds up rapidly, and the output process is smooth, with no significant overshoot. Under different braking percentage conditions, the simulation accuracy of both braking pressure and response time exceeds 95%, indicating that the established model accurately reflects actual braking performance and provides a theoretical basis for optimizing tracked vehicle braking systems. Finally, by rationally designing the parameters of the accumulator and electro-hydraulic proportional valve and reducing the brake cylinder volume, it is possible to improve braking performance. This provides a theoretical basis for the optimization of tracked vehicle braking systems. Full article

This article presents the synthesis, real-time implementation, and experimental validation of an approximated adaptive dynamic programming (AADP) actor–critic controller for precise flow rate regulation of a variable-displacement axial-piston pump designed for open-circuit hydraulic systems. Replacing the conventional hydro-mechanical regulator with an electrohydraulic proportional spool valve, the model-free controller employs two compact two-layer neural networks: the actor generates valve PWM signals from the flow tracking error, its integral, and measured discharge pressure, while the critic approximates the infinite-horizon quadratic cost-to-go via the online solution of the Bellman equation through gradient descent on Bellman residuals. Lyapunov analysis establishes closed-loop stability under bounded learning rates, with initial weights tuned via nominal plant simulation to ensure convergence from feasible starting policies. After extensive laboratory testing across four fixed loading conditions and dynamic load variations, the adaptive controller demonstrated superior performance compared with a proportional-integral (PI) controller, a Lyapunov model-reference adaptive controller (LMRAC), and an H_∞ controller (Hinf). Real-time metrics confirm bounded critic signals and near-zero Bellman errors, validating optimal policy convergence amid unmodeled hydraulic nonlinearities. Full article

Traditional passive hydraulic dampers face the challenges of extended design cycles, inefficient parameter matching, and fixed performance, limiting their adaptability. This paper proposes an integrated solution that combines inverse parametric design with active, continuously adjustable damping. First, a high-fidelity nonlinear model is developed based on valve plate elasticity and multi-valve coupling dynamics, achieving a simulation error of ≤4%. An improved genetic algorithm is then designed to inversely optimize five key parameters. This optimization reduces the deviation between the prototype’s damping force–velocity characteristics and the target curve to ≤3% and shortens the design cycle by approximately 40%. Building on this foundation, a pilot-operated electro-hydraulic proportional relief valve is integrated to enable continuous damping adjustment. Co-simulation using AMESim2404 and MatlabSimulinkR2022 reveals the influence of solenoid valve parameters on damping characteristics and calibrates the current–damping force mapping. A co-simulation of a skyhook-controlled quarter-vehicle model demonstrates that the semi-active suspension system reduces the root mean square (RMS) of vertical body acceleration by 21.7%, indicating a significant theoretical improvement in ride comfort. This study establishes a complete technical pathway of “modeling → inverse optimization → integration → verification,” providing an efficient and viable core component solution for intelligent suspension systems. Full article

This paper focus on the high accuracy force control of electro-hydraulic proportional load simulator (EHPLS). Firstly, to weaken the influence of the unknown dead zone of the proportional valve, a mathematic model with a smooth inverse dead zone was constructed. Then, finite-time prescribed performance function, of which the desired steady-state value can be achieved within finite time, is defined to impose constraints on the tracking error, while the neural network feedback is introduced to compensate for the unknown dynamic, which can ensure the tracking accuracy further improved for the entire tracking process in the presence of unknown dead-zone parameters, unknown system parameters and disturbance. Finally, through design modification, the proposed control technologies are realized based on the output feedback signal. Comparative simulations under two desired force trajectories are carried out to verify the effectiveness of the proposed controller. 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

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article

This paper addresses a recursive adaptive anti-lock braking (AB) control design problem for electro-hydraulic brake (EHB) systems subject to unknown tire–road-friction coefficients and disturbances. Compared with the relevant literature, the primary contributions are (i) the development of a novel nonlinear AB model integrated with a bond-graph-based EHB (BGEHB) system, and (ii) the proposal of an adaptive neural AB control approach incorporating a nonlinear disturbance observer (NDO). The AB and BGEHB models are unified into a single nonlinear braking model, with the wheel speed as the system output and the duty ratios of the BGEHB inlet and outlet valves as control inputs. To maintain an optimal slip ratio during braking, we design the NDO-based adaptive AB controller to regulate the wheel speed in a recursive manner. The designed controller incorporates a delay-compensation term to address the time-delay characteristics of the hydraulic system, employs a neural-network function approximator in the NDO and controller to compensate for the unknown tire–road-friction coefficient, and applies NDOs to compensate for disturbances due to the vehicle motion and BGEHB dynamics. The stability of the proposed control scheme is established via the Lyapunov theory, and a simulation comparison is presented to demonstrate the effectiveness of the proposed design approach. 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

The accurate execution of aeroengine flight environment simulation tests relies on the electro-hydraulic servo valve control system in the altitude test facility. However, time delays arising from various factors, such as friction or sensor latency, impose significant constraints on system responsiveness and control precision. To address this challenge, an enhanced active disturbance rejection control has been developed. The proposed method employs an improved output prediction constructed by tracking differentiator to mitigate delay effects, introduces the Taylor compensator to more accurately capture future signal trends, and incorporates a dynamic adjustment mechanism based on error variation to optimize the parameters of the extended state observer in real time, thereby enhancing robustness under varying operating conditions. The simulation results demonstrate that under fixed-delay conditions, the proposed algorithm exhibits fast response characteristics; under varying-delay conditions, unlike model-dependent approaches, it remains less affected by delay fluctuations and maintains superior response speed and stability, thereby ensuring the accuracy of flight environment simulation tests. Full article

The article is devoted to the synthesis, implementation, simulation and experimental study of a real-time Lyapunov-based two-degree-of-freedom model reference adaptive controller (MRAC) for an axial-piston pump. The controller of the developed real-time system determinates control signal values applied to the electro-hydraulic proportional valve. The proportional valve is an actuator for driving the swash plate swivel angle of the pump. The swash plate swivel angle determines the displacement volume of the flow rate of the pump. The MRAC is synthesized based on the experimentally identified mathematical model. To conduct the identification and experimental investigation of the controller, the authors have used an existing laboratory test setup. The comparison of the designed MRAC with conventional PI controller is performed. The control performance analysis is based on integral square error (ISE) in transient responses of the pump flow rate at different flow rate references and loads. Full article

This paper presents a coordinated control strategy for an electro-hydraulic composite braking system in in-wheel motor electric vehicles to enhance regenerative energy recovery and braking safety. A novel hydraulic control unit (HCU) without a pressure-reducing valve is designed to simplify structure and maximize energy utilization. Based on the ideal braking force distribution, a force allocation strategy coordinates motor and hydraulic braking across modes, ensuring motor torque can compensate total braking torque when wheel lock occurs. An anti-lock braking (ABS) strategy relying solely on motor torque adjustment is proposed, keeping hydraulic torque constant while rapidly stabilizing slip within 13–17%, thereby avoiding interference between hydraulic and motor braking. A joint Simulink–AMESim–CarSim platform evaluates the strategy under varying conditions, and real-vehicle tests in regenerative mode confirm feasibility and smooth switching. Results show the proposed approach achieves target braking intensity, improves energy recovery, reduces torque oscillations and valve actions, and maintains stability. The study offers a practical solution for integrating regenerative braking and ABS in in-wheel motor EVs, with potential for hardware-in-the-loop validation and advanced stability control applications. Full article

With the rapid progress of industrial technology in recent years, servo controllers have the characteristics of precise control and short response time and are widely used in different industrial fields. As for the electro-hydraulic servo valve being an important control element of the entire hydraulic system, the quality of its own characteristics has a significant impact on the normal operation and safety of the mechanical equipment. Therefore, the working stability of the servo valve in actual operation is of great importance to its body and the overall servo system. Similarly, during the vibration test of the electro-hydraulic servo shaking table, servo valve inevitably experiences various vibrations and shocks, which requires the servo system to be able to withstand the test and assessment under the extreme conditions in actual operation to ensure the smooth operation. This paper takes function of the shaker as the research target and studies the servo valve under various vibration conditions by constructing a digital modeling system. On this basis, an adaptive format filter is established, and corresponding vibration suppression methods are adopted for the vibration conditions inside the system. Finally, simulation examples are used to prove that this method can more effectively control the vibration in the servo valve and suppress the interference with shaking table function. Full article