Jianyong Yao

This paper proposes an adaptive prescribed-time tracking control strategy based on the dynamic surface technique for hydraulic servo systems subject to time-varying parameters, external disturbances, and output constraints. First, a state-constrained transformation function is introduced to convert the strict output constraint condition into an error boundedness problem. Meanwhile, the dynamic surface control (DSC) technique is employed to effectively avoid the “explosion of complexity” inherent in traditional backstepping design. Second, to tackle complex uncertainties, prescribed-time-driven adaptive update and disturbance estimation laws are separately formulated for precise parameter learning and active disturbance feedforward compensation. Furthermore, a smooth nonlinear robust term is specifically integrated to suppress the residual errors induced by parameter adaptation. Based on the transformed system, a novel control framework integrating error constraints, adaptive parameter estimation, and prescribed-time performance is developed. Rigorous Lyapunov stability analysis proves that the proposed controller not only strictly prevents the system output from violating the constraint boundaries throughout the entire operation, but also ensures that the tracking error converges rapidly and smoothly to a small bounded region near the origin within a time that can be independently predetermined by the designer. Finally, the effectiveness and superiority of the proposed control strategy are fully validated through simulations.

The presence of unknown or difficult-to-identify dynamics and disturbances in robotic manipulators has long posed a challenge for high-precision control design. While deep neural networks (DNNs) offer a potential solution by approximating these unknown dynamics, they typically act as black-box models, and the approximations they produce often do not conform to physical principles, limiting interpretability and reliability in control applications. Toward this end, this study incorporates established robotic physics knowledge into the deep learning architecture and develops a physics-guided deep learning (PGDL) controller. The PGDL module estimates and compensates for unknown system dynamics online while enforcing physically meaningful structure, thereby improving the accuracy of feedforward compensation. In addition, a continuous nonlinear robust adaptive feedback term is introduced to handle residual modeling errors and external disturbances, further enhancing overall control performance. Lyapunov-based stability analysis confirms that the proposed PGDL-based control scheme ensures asymptotic convergence of the tracking errors. The effectiveness of the proposed controller was validated by comparative simulations and experiments.

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.

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.

This article proposes an adaptive dynamic surface prescribed‐time control strategy for uncertain full‐state constrained nonlinear systems subject to time‐varying parameter uncertainties and external disturbances. A smooth nonlinear term with a unified adaptive‐gain law is first constructed to simultaneously enable compensation for the time‐varying parameter uncertainties and external disturbances. Subsequently, by employing the dynamic surface control and the constraint transformation function, an appropriate prescribed‐time controller framework with an improved first‐order filter is given, where the complexity explosion can be avoided and all the system states can be restricted to the predefined bounds. Rigorous theoretical analysis ensures both the prescribed‐time transient performance and steady performance of the tracking errors while achieving the full‐state constraints. Finally, the effectiveness of the proposed controller is verified by two simulation examples.

In this paper, a command filter-based adaptive prescribed-time control method is proposed for hydraulic servo systems subject to time-varying parameters, external disturbances and output constraints. Firstly, a state-based nonlinear transformation function is introduced to convert the output-constrained problem into a boundedness problem. Then, an auxiliary system is constructed to compensate for command filtering errors. Subsequently, to handle the uncertainties from time-varying parameters and external disturbances, a smooth nonlinear term featuring an updated gain and incorporating a prescribed-time function is designed. Based on the transformed system, a novel control framework integrating command filtering, adaptive control, and the prescribed-time function is developed. Consequently, the complexity explosion is avoided, and the system output is guaranteed to converge to a small bounded interval near zero while strictly satisfying the output constraints. Moreover, this prescribed convergence time can be independently set by the designer. Furthermore, both the transient convergence performance within the prescribed time and the bounded convergence performance afterward are guaranteed by Lyapunov stability analysis. Finally, the effectiveness of the proposed method is verified by simulation results.