Distributed tracking control for heterogeneous multi-agent systems via data-driven parameterisation approach

AbstractThe distributed tracking control problem for heterogeneous multi-agent systems with unknown system dynamics is investigated. The objective is to provide a data-driven distributed tracking control protocol that ensures tracking performance between agents and the leader. To this end, the concept of data informativity for matching conditions is introduced. Then, the data-based sufficient and necessary conditions to achieve state tracking are provided. Meanwhile, a data-driven parameterisation approach for designing the distributed tracking control protocol is given. Compared with previous results, the reference input is considered in the leader's dynamics, and the computational burden is reduced by solving a set of data-based equations and inequality constraints rather than iteration. Additionally, the developed results are still appropriate for handling the tracking control issue of the single linear system, and the current constraint that the reference system be stable is eased. Finally, two simulation examples are given to verify the proposed schemes' effectiveness.Keywords: Data-driven parameterisationheterogeneous multi-agent systemsdistributed tracking control protocolmatching conditions Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB3304800, the National Natural Science Foundation of China under Grant U21A20475, 61873050, the Fundamental Research Funds for the Central Universities under Grant N2304007, and the Research Fund of State Key Laboratory of Synthetical Automation for Process Industries under Grant 2018ZCX14.Notes on contributorsWenli ChenWenli Chen received the B.S. degree in mechanical and electrical engineering from Guangzhou University, Guangzhou, China, and M.S. degree in automation from Guangdong University of Technology, Guangzhou, China. He is currently pursuing the Ph.D. degree in control theory and engineering with Northeastern University, Shenyang, China. His research interests include data-driven control, distributed learning and optimisation, and cyber-physical systems.Xiaojian LiXiaojian Li received the B.S. and M.S. degrees in mathematics from Northeast Normal University, Changchun, China, in 2003 and 2006, respectively, and the Ph.D. degree in control theory and engineering from Northeastern University, Shenyang, China, in 2011. He is currently a Professor with the College of Information Science and Engineering, Northeastern University. His current research interests include fault diagnosis, fault-tolerant control, fuzzy control, and cyber-physical systems.