Jianyong Yao

Hierarchical Control of Hydraulic Manipulators via Identifier–Critic Scheme and Robust Control

High-precision motion control of multidegree-of-freedom (multi-DOF) hydraulic manipulators remains a significant challenge due to their highly coupled mechanical dynamics and strong nonlinearities. To address these difficulties, this article proposes a hierarchical control framework to effectively deal with the rigid-body dynamics and actuator-level nonlinearities of hydraulic manipulators. In the upper level controller, an identifier–critic neural network is employed to estimate the manipulator dynamics online under an interval excitation condition. Based on the identified model, an approximate optimal position control policy is generated by minimizing a predefined performance cost function, enabling adaptive treatment of dynamic coupling and modeling uncertainties. At the lower level, a model-based robust nonlinear control law is designed for the hydraulic actuators to ensure exponentially convergent force tracking, thereby mitigating the effects of nonlinearities, disturbances, and unmodeled dynamics. The effectiveness of the proposed hierarchical control framework is validated through comparative experiments on a 6-DOF hydraulic manipulator. Experimental results demonstrate superior tracking accuracy and transient response compared with baseline methods, highlighting the potential of the proposed approach for high-precision motion control of complex hydraulic manipulators.

Prescribed‐Time Tracking Control of Nonlinear Strict Feedback Systems With Time‐Varying Parameters and Disturbances Under Full‐State Constraints

Adaptive Prescribed-Time Tracking Control for Output-Constrained Hydraulic Servo Systems with Time-Varying Parameters

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.