Data-driven cooperative optimal output regulation for linear discrete-time multi-agent systems by online distributed adaptive internal model approach  

作　　者：Kedi XIE Yi JIANG Xiao YU Weiyao LAN 

机构地区：[1]Department of Automation,Xiamen University,Xiamen 361005,China [2]Department of Electrical Engineering,City University of Hong Kong,Hong Kong 999077,China [3]Key Laboratory of Control and Navigation(Xiamen University),Fujian Province University,Xiamen 361005,China

出　　处：《Science China(Information Sciences)》2023年第7期26-41,共16页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key R&D Program of China(Grant No.2021ZD0112600);National Natural Science Foundation of China(Grant Nos.61873219,62173283);Natural Science Foundation of Fujian Province of China(Grant No.2021J01051)。

摘　　要：In this study,a data-driven learning algorithm was developed to estimate the optimal distributed cooperative control policy,which solves the cooperative optimal output regulation problem for linear discretetime multi-agent systems.Notably,the dynamics of all the agent systems and exo-system is completely unknown.By combining adaptive dynamic programming with an internal model,a model-free off-policy learning method is proposed to estimate the optimal control gain and the distributed adaptive internal model by only accessing the measurable data of multi-agent systems.Moreover,different from the traditional cooperative adaptive controller design method,a distributed internal model is approximated online.Convergence and stability analyses show that the estimate controller generated by the proposed data-driven learning algorithm converges to the optimal distributed controller.Finally,simulation results verify the effectiveness of the proposed method.

关 键 词：adaptive dynamic programming cooperative control distributed adaptive internal model multi-agent systems optimal output regulation 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]