文献类型：期刊文献

英文题名：Adaptive Control for Multi-Agent Systems with Actuator Fault Via Reinforcement Learning and its Application on Multi-Unmanned Surface Vehicle

作者：Zhang, Wenjun[1]; Bai, Weiwei[1]; Cao, Liang[2]; Liu, Qiang[1]

机构：[1] Navigation College, Dalian Maritime University, Dalian, 116026, China; [2] Maritime College, Guangdong Ocean University, Zhanjiang, 524000, China

年份：2022

外文期刊名：SSRN

收录：EI(收录号：20220451069)

语种：英文

外文关键词：Actuators - Adaptive control systems - Closed loop systems - Controllers - Gradient methods - Learning systems - Multi agent systems - Neural networks - Reinforcement - Unmanned surface vehicles

外文摘要：In this article, the adaptive cooperative control design problem is studied for a class of second order multi-agent systems (MASs) with actuator fault via reinforcement (RL). The strategic utility function is approximated by the critic neural network (NN), and the uncertain dynamics in MASs are estimated by the active NN. The NN weight vectors are updated by employing the gradient descent strategy. Then, the distributed RL control strategy is developed to solve the consensus control design problem. In comparison with the existent RL control results, the actuator fault is taken into consideration in the controller design. The stability analysis is given based on the Lyapunov theory, and all the signals in the closed-loop system are guaranteed to semi-globally uniformly ultimately bounded (SGUUB). Two simulation examples that include a multi-unmanned surface vehicle system are presented to demonstrate the validation of this strategy. ? 2022, The Authors. All rights reserved.