Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning

Abstract

This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method.