Research on Motion Control of Hydraulic Manipulator Based on Prescribed Performance and Reinforcement Learning

Achieving high-precision motion control for hydraulic manipulators presents a challenging task. Addressing the issue of low motion control accuracy caused by the strong electromechanical-hydraulic coupling characteristics of hydraulic manipulator systems, this paper innovatively introduces an RBF neural network and an Actor–Critic reinforcement learning architecture within a performance-based control framework designed using the inverse method. This approach enables dual compensation for both internal uncertainties and external disturbances within the manipulator, thereby enhancing the system’s control performance. First, within the control architecture, the performance function ensures system transient performance while employing an RBF neural network to estimate and compensate for internal unmodeled errors caused by mechanical coupling and hydraulic parameter uncertainties. Stability proofs are used to derive the network weight update rate. Second, a disturbance compensator is designed based on reinforcement learning. Deployed into the controller through offline training and online adaptation, it compensates for external system disturbances, further improving control accuracy. Finally, comparative and ablation experiments conducted on a hydraulic manipulator testbed demonstrate the effectiveness of the disturbance compensator. Compared to PID control, the proposed approach achieves a 60–65% improvement in control accuracy.