Actor–critic learning based coordinated control for a dual-arm robot with prescribed performance and unknown backlash-like hysteresis. (July 2022) - Ouyang, Yuncheng; Sun, Changyin; Dong, Lu
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. Highlights: The ACL based coordinated control improves the learning ability of the DAR system. Prescribed performance is imposed on the system in the ACL for performance guarantee. The problem of the hysteresis is handled by the ACL based coordinated control.