文献类型：期刊文献

英文题名：Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks With Unknown Dynamics

作者：Jin, Xin[1,2];Mao, Shuai[1];Kocarev, Ljupco[3,4];Liang, Chen[1];Wang, Saiwei[1];Tang, Yang[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 201210, Peoples R China;[3]Macedonian Acad Sci & Arts, Skopje 1000, North Macedonia;[4]Univ Sv Kiril & Metodij, Fac Comp Sci & Engn, Skopje 1000, North Macedonia

年份：2022

卷号：9

期号：5

起止页码：3701

外文期刊名：IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

收录：;EI(收录号：20222412226479);WOS:【SCI-EXPANDED(收录号:WOS:000852246800060)】；

基金：This work was supported in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61725301, in part the National Natural Science Foundation of China Key Program under Grant 62136003, in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300, in part by the China Postdoctoral Science Foundationunder Grant 2021TQ0107, and in part by the Programme of Introducing Talents of Discipline to Universities the 111 Project under Grant B17017 and Shanghai AI Lab.

语种：英文

外文关键词：Reinforcement learning; Neural networks; Mathematical models; Attitude control; System dynamics; Manipulator dynamics; Costs; Optimal attitude consensus; multiple rigid body networks; event-triggered control; reinforcement learning

摘要：In this paper, an event-triggered Reinforcement Learning (RL) method is proposed for the optimal attitude consensus of multiple rigid body networks with unknown dynamics. Firstly, the consensus error is constructed through the attitude dynamics. According to the Bellman optimality principle, the implicit form of the optimal controller and the corresponding Hamilton-Jacobi-Bellman (HJB) equations are obtained. Because of the augmented system, the optimal controller can be obtained directly without relying on the system dynamics. Secondly, the self-triggered mechanism is applied to reduce the computing and communication burden when updating the controller. In order to address the problem that the HJB equations are difficult to solve analytically, an RL method which only requires measurement data at the event-triggered instants is proposed. For each agent, only one neural network is designed to approximate the optimal value function. Each neural network is updated only at the event-triggered instants. Meanwhile, the Uniformly Ultimately Bounded (UUB) of the closed-loop system is obtained, and Zeno behavior is also avoided. Finally, the simulation results on a multiple rigid body network demonstrate the validity of the proposed method.