Adaptive RISE Control of Hydraulic Manipulators Using Actor-Critic Architecture

Aiming at the difficulty of high-precision motion control of multi-degree-of-freedom (DOF) heavy-duty hydraulic manipulator, an adaptive robust integral of the sign of the error (RISE) controller based on Actor-Critic is developed to achieve high-performance progressive tracking of the system under various unmodeled errors and unknown disturbance. In this article, the proposed design consists of two frameworks: A RISE control method provides a closed-loop system stability framework; A reinforcement learning (RL) with Actor-Critic idea is introduced into the RISE control architecture to improve the system tracking accuracy. Specifically, two multilayer neural network (NN) estimators based on Actor-Critic architecture are designed to deal with uncertain coupled mechanical dynamics and nonlinear hydraulic dynamics, respectively. Specifically, the performance evaluation function is constructed by using the critic NN (CNN) to estimate the cumulative tracking error of the system. The minimum excitation signal of the cumulative error of CNN feedback is integrated in the actor NN (ANN) and the weight update law is learned based on the gradient descent method. Feedforward compensation is performed on the unknown dynamics to reduce the high feedback gain. At the same time, RISE control is used to deal with the reconstruction error and interference of the NN to ensure the asymptotic stability of the system. Finally, an experimental study is carried out based on a 3-DOF heavy-duty hydraulic manipulator to verify the effectiveness of the proposed controller.