Decentralized Robust Zero-sum Optimal Tracking Control of Modular Robot Manipulator based on Event-Triggered with Uncertain Environment

Abstract

In this paper, the problem of decentralized robust zero-sum optimal tracking control based on event-triggered for modular robot manipulators (MRMs) systems in contact with uncertain environments is studied. Based on the joint torque feed-back (JTF) technique, the dynamic model of the MRMs subsystem is established. Taking the external uncertain environment as a control input, the robust optimal control problem is transformed into a two-player zero-sum game optimal control problem. According to the adaptive dynamic programming (ADP) algorithm, the optimal control law and the worst disturbance law under time-triggered are obtained. In order to achieve more accurate control, part of the known model information is used in the controller design, and the decomposition-based robust controller is used to manage the model uncertainty. For the purpose of reducing the computational burden, event-triggered is used in controller design. By constructing the critic neural network , the optimal compensation control law can be obtained. The stability of the closed-loop MRM system is proved by using Lyapunov theory. Finally, the effectiveness of the proposed method is verified by experimental results.