Actor–critic learning based coordinated control for a dual-arm robot with prescribed performance and unknown backlash-like hysteresis

Abstract

In this paper, we focus on the tracking problem of a dual-arm robot (DAR) with prescribed performance and unknown input backlash-like hysteresis. Considering this problem, adaptive coordinated control with actor–critic (AC) design is proposed. Motivated by the increasing control requirements, prescribed performance is imposed on the DAR system to guarantee the tracking performance. In order to improve the self-learning ability and handle the problems caused by the input backlash-like hysteresis and system uncertainty, AC learning (ACL) algorithm is introduced. Through the cost function about tracking errors, a critic network is adopted to judge the control performance. An actor network is adopted to obtain the control input based on the critic result, where the system uncertainty and unknown part of the input backlash-like hysteresis are approximated by neural networks (NNs). In addition, the system stability is proven by the Lyapunov direct method. Numerical simulation is finally conducted to further testify the validity of the proposed coordinated control with AC design for the DAR system.