OPTIMAL TRACKING CONTROL FOR ROBOT MANIPULATORS WITH INPUT CONSTRAINT BASED REINFORCEMENT LEARNING

This paper introduces an optimal tracking controller for robot manipulators with saturation torques. The robot model is presented as a strict-feedback nonlinear system. Firstly, the position tracking control problem is transformed into the optimal tracking control problem. Subsequently, the saturated optimal control law is designed. The optimal control law is determined through the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We use a reinforcement learning algorithm with only one neural network (NN) to approximate the solution of the equation HJB. The technique of experience replay is used to relax a persistent citation condition. By Lyapunov analysis, the tracking and the approximation errors are uniformly ultimately bounded (UUB). Finally, the simulation on a robot manipulator with saturation torques is performed to verify the efficiency of the proposed controller.