Search Results (143)

With the development of electrification and green technologies in construction machinery, servo pump-controlled technology has shown great potential in electric loaders due to its high efficiency and energy-saving characteristics. However, the complex coupling among multiple parameters makes it difficult to simultaneously optimize dynamic response and energy efficiency. To address this issue, a multi-objective parameter optimization method for servo pump-controlled units based on Sobol sensitivity analysis and the NSGA-II algorithm is proposed. First, an electro-hydraulic coupling model of the servo pump-controlled unit is established. Subsequently, Sobol global sensitivity analysis is employed to identify sensitive parameters affecting system performance, and a multi-objective optimization model is constructed with dynamic performance and energy efficiency as optimization objectives. Finally, the NSGA-II algorithm is adopted for coordinated parameter optimization, and the results are validated through MATLAB/Simulink simulations and experiments. Results show that the optimized system achieves improved dynamic response, stability, and energy efficiency, demonstrating the effectiveness of the proposed method for the parameter optimization of electric loader servo pump-controlled systems. Full article

The underground caverns of pumped-storage power stations generally feature large inclination angles. During the bottom-up oblique excavation by hard-rock Tunnel Boring Machines (TBMs), the Anti-Back-Slip (ABS) support system is the core device ensuring safe operations. Specifically, the synchronization of the multiple hydraulic cylinders within the ABS system is a critical factor determining the stability and safety of the TBM. Therefore, this paper designs a hydraulic control system for the ABS device and proposes an adjacent cross-coupling synergistic control strategy based on adaptive backstepping. This strategy innovatively integrates an adaptive backstepping control law into the adjacent cross-coupling topology to achieve high-precision multi-cylinder control. Utilizing the AMESim-Simulink platform, high-fidelity co-simulations are conducted under both uniform and eccentric load conditions. The results demonstrate that under nominal conditions, the proposed algorithm exhibits asymptotic convergence at the mathematical level. The system maintains robust stability under dynamic excitations. When subjected to sudden asymmetric eccentric loads of 1.0–2.0 times, the system prevents tracking divergence and limits the maximum multi-cylinder synchronization error to within 1.82 mm. This research satisfies the requirements for synchronous control and provides a theoretical and engineering reference for the disturbance-rejection synergy of inclined shaft TBM support systems. Full article

This paper investigates a class of electro-hydraulic servo systems with unknown nonlinear functions and parameters. To address the issues of modeling uncertainties and unmodeled dynamics, an adaptive robust nonlinear controller integrating neural networks and predefined-time prescribed performance is proposed. First, an exponential-type predefined-time prescribed performance function is designed to ensure that the system tracking error converges to a prescribed region within a predefined time. An adaptive law based on the discontinuous projection method is developed to estimate unknown parameters and compensate for them in the controller. The dynamic surface technique is introduced to overcome the “explosion of complexity” problem inherent in the traditional backstepping method. Meanwhile, neural networks are employed to approximate system nonlinearities, thereby reducing modeling errors. Finally, the stability of the closed-loop system is rigorously proved using Lyapunov theory, and numerical simulations validate the superiority of the designed controller over conventional control strategies. Full article

Electro-hydraulic servo systems are widely used in industrial machinery and automation due to their high power density and fast dynamic response; however, their achievable positioning accuracy is often limited by nonlinear friction effects. In many robust control strategies, including sliding mode control and its multi-surface variants, friction is commonly treated as a lumped bounded disturbance. This simplification neglects the dynamic and operating condition-dependent nature of friction, leaving the practical value of explicit friction compensation insufficiently clarified, especially for electro-hydraulic actuators operating near their bandwidth limits. This paper presents an experimental evaluation of LuGre-based dynamic friction compensation integrated into a multi-surface sliding mode control framework for electro-hydraulic actuators. Rather than proposing a new control methodology, the study focuses on clarifying, from a control-oriented mechanical engineering perspective, how friction compensation influences closed-loop tracking performance under different operating regimes. The proposed scheme is implemented on a laboratory-scale electro-hydraulic test bench and evaluated using step and sinusoidal reference motions over a wide range of excitation frequencies, from low-speed operation to the practical bandwidth limit of the actuator. Comparative experiments with a conventional proportional–integral–derivative controller and a multi-surface sliding mode controller without friction compensation are conducted to isolate the effect of explicit friction modeling. The experimental results reveal a strongly frequency-dependent influence of friction on tracking performance. At low excitation frequencies (e.g., 0.1 Hz), friction compensation provides only marginal improvement in root mean square (RMS) tracking errors. In contrast, as the excitation frequency approaches the actuator bandwidth limit (1 Hz), explicit LuGre-based friction compensation reduces the relative RMS tracking error by approximately 57% compared with the baseline MSSM controller and by up to 82% relative to a conventional PID controller. These results demonstrate that the effectiveness of friction compensation is highly dependent on operating conditions, providing experimentally grounded guidance for the design of control strategies for bandwidth-limited electro-hydraulic machines. Full article

In research on electro-hydraulic servo systems, nonlinearity deeply affects dynamic performance, such as the output of hydraulic actuators and the generation of control signals, leading to response hysteresis and control complexity. Moreover, during the control process, changes in the external environment and component loss lead to model parameter distort, which reduces control capability. To address these challenges, this paper conducts a structural transformation on the traditional dynamic surface controller in combination with the fast finite-time stability theorem and proposes a novel finite-time dynamic surface control strategy, which can not only overcome the differential explosion phenomenon in the recursive backstepping iterative process but also enhance the transient dynamic response speed. Furthermore, the neural network adaptive algorithm is adopted to handle the negative dynamic effect caused by parametric uncertainty. The theoretical results are verified by the Lyapunov stability method and numerical simulation. 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

Physical coupling, nonlinearity and uncertainty degrade the dynamic performance of electro-hydraulic servo systems, particularly under conditions involving input delays, leading to reduced trajectory tracking accuracy or even system instability. These factors often fail to meet the high-precision requirements of engineering applications. To effectively address these difficulties, this paper proposes a novel adaptive control protocol for networked electro-hydraulic servo systems. For the minimal communication delay problem of networked electro-hydraulic servo systems, Laplace transform algorithm together with Padé approximation is adopted in this study to remove the delay term from the mathematical system model. Moreover, the matched modeling parametric uncertainty of systems is estimated and compensated by the neural network adaptive method to improve the dynamical performance of the system during the steady state. The controller is designed on the basis of recursive backstepping strategy and the finite-time stability theorem, which can handle system nonlinearity and guarantee transient response. The validity of the proposed theoretical results is proved by Lyapunov stability and the feasibility and superiority are verified via physical simulation. 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

To address performance degradation and control instability in electro-hydraulic servo active suspension systems due to internal leakage faults arising from wear and aging of hydraulic components, this paper proposes an innovative fuzzy PID fault-tolerant controller based on the Improved Giant Armadillo Optimization (IGAO) algorithm. Specifically, to overcome the limitations of the standard Giant Armadillo Optimization (GAO), which is prone to local optima and exhibits poor convergence performance when handling multi-constraint parameter optimization problems, this study introduces a nonlinear dynamic inertia weight mechanism and a random reflection strategy for out-of-bounds particles to improve the original algorithm’s performance. These enhancements significantly enhance its ability to balance global exploration and local exploitation. Furthermore, this research develops a comprehensive performance evaluation fitness function by quantifying key performance indicators such as body acceleration, suspension dynamic deflection, and tire dynamic load. A quarter-car model incorporating an internal leakage fault was established as a simulation validation platform to demonstrate the reliability of the proposed method. Simulation results indicate that under various road excitation conditions, the proposed IGAO algorithm can rapidly and stably converge to superior parameters for the fuzzy PID controller. Compared to the Particle Swarm Optimization (PSO) and standard GAO algorithm, the control system optimized by IGAO not only significantly more effectively suppresses body vibration and reduces shock amplitude but also exhibits stronger dynamic recovery performance and control robustness under varying degrees of internal leakage faults. This research provides a robust control approach for addressing internal parameter uncertainties in hydraulic systems and offers a new approach to theoretical modeling for enhancing the reliability of design and fault-tolerant control capabilities of active suspension systems. Full article

In this paper, a nonlinear integral sliding-mode controller (SMC) based on a radial basis function (RBF) neural network is proposed to address the challenges of high nonlinearity, parameter uncertainty, and unmodeled dynamics in the electro-hydraulic servo system of a robotic excavator. The controller design incorporates adaptive RBF neural networks to compensate for system perturbations and uncertain nonlinearities, while an integral sliding surface is employed to eliminate steady-state error. This approach not only compensates for uncertainties but also reduces the traditional SMC’s high dependency on precise system parameters. The mathematical model of the bucket electro-hydraulic servo system is established without linear approximation. Based on this model, the sliding-mode controller with RBF neural networks (SMC-RBF) is designed, and its asymptotic stability is proven using the Lyapunov method. Simulation and experimental results are compared with a traditional PID controller to verify the proposed controller’s superiority. The simulations show that the SMC-RBF controller meets the requirements for tracking performance and demonstrates robustness, improving sinusoidal tracking performance by 46% compared to the PID controller. Experimental results further demonstrate that the SMC-RBF controller improves the trajectory accuracy for a two-meter straight line by 52.46% in comparison to the traditional PID controller. 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

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

This paper presents the design of a fuzzy PID controller with a parallel structure for controlling an electro-hydraulic servo system. The main factors affecting control performance in electro-hydraulic systems are discussed in detail. The proposed fuzzy controller features a specific structure obtained through a coefficient transfer approach from a classical PID controller, enabling seamless integration of the fuzzy logic component and simplifying the tuning process. Relevant mathematical equations and dependencies are provided. The closed-loop system’s stability is analyzed using the BIBO (Bounded Input, Bounded Output) criterion. The designed controller is implemented in the MATLAB/Simulink 2019 environment and tested using a real-time measurement and control system. Graphical results are presented, illustrating the performance of the closed-loop system under step and sinusoidal reference signals. The obtained results confirm the qualities and proper tuning of the implemented controller. 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