Optimal Synchronization Control of Heterogeneous Asymmetric Input-Constrained Unknown Nonlinear MASs via Reinforcement Learning

作     者：Lina Xia Qing Li Ruizhuo Song Hamidreza Modares Lina Xia;Qing Li;Ruizhuo Song;Hamidreza Modares

作者机构：Beijing Engineering Research Center of Industrial Spectrum ImagingSchool of Automation and Electrical EngineeringUniversity of Science and Technology Beijing IEEE the Department of Mechanical EngineeringMichigan State University 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第3期

页      面：520-532页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

基　　金：supported in part by the National Natural Science Foundation of China(61873300,61722312) the Fundamental Research Funds for the Central Universities(FRF-MP-20-11) Interdisciplinary Research Project for Young Teachers of University of Science and Technology Beijing(Fundamental Research Funds for the Central Universities)(FRFIDRY-20-030) 

主　　题：Asymmetric input-constrained heterogeneousnonlinear multiagent systems(MASs) Hamilton-Jacobi-Bellman(HJB)equation novel observer reinforcement learning(RL) 

摘      要：The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems(MASs)is considered in the ***,a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original ***,considering that the leader’s information is not available to every follower,a novel distributed observer is designed to estimate the leader’s state using only exchange of information among neighboring *** that,a network of augmented systems is constructed by combining observers and followers dynamics.A nonquadratic cost function is then leveraged for each augmented system(agent)for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman(HJB)equation is solved in a data-based *** specifically,a data-based off-policy reinforcement learning(RL)algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents’*** of the improved RL algorithm to the solution to the constrained HJB equation is also ***,the correctness and validity of the theoretical results are demonstrated by a simulation example.