{"title":"Passivity-Based Formation Control for Fractional-Order Multiagent Systems With and Without Communication Delay","authors":"Jin-Liang Wang;Lina Huang;Shun-Yan Ren;Tingwen Huang","doi":"10.1109/TETCI.2024.3386837","DOIUrl":null,"url":null,"abstract":"Because fractional-order differential equations can more accurately model the dynamics of agents, it is very meaningful to investigate the formation control for fractional-order multi-agent systems (FOMASs). Recently, passivity has been widely utilized to tackle various cooperative control problems for integer-order MASs since passive systems are internally stable. Apparently, it is also advantageous to deal with the formation control problem of FOMASs based on the passivity. In this paper, two types of formation control problems for second-order nonlinear FOMASs are discussed by employing the passivity, that is, the cases without and with communication delay, respectively. On the basis of the devised state feedback and adaptive state feedback controllers, several passivity conditions for single fractional-order agent are derived. Furthermore, by selecting suitable distributed state feedback control strategy and exploiting the passivity of fractional-order agent, two formation criteria for the FOMAS are given. In addition, the above obtained results are further extended to the case where communication delay exists in the control input. Finally, two numerical examples are provided to substantiate the effectiveness of the derived formation criteria.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"4143-4154"},"PeriodicalIF":5.3000,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10505745/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Because fractional-order differential equations can more accurately model the dynamics of agents, it is very meaningful to investigate the formation control for fractional-order multi-agent systems (FOMASs). Recently, passivity has been widely utilized to tackle various cooperative control problems for integer-order MASs since passive systems are internally stable. Apparently, it is also advantageous to deal with the formation control problem of FOMASs based on the passivity. In this paper, two types of formation control problems for second-order nonlinear FOMASs are discussed by employing the passivity, that is, the cases without and with communication delay, respectively. On the basis of the devised state feedback and adaptive state feedback controllers, several passivity conditions for single fractional-order agent are derived. Furthermore, by selecting suitable distributed state feedback control strategy and exploiting the passivity of fractional-order agent, two formation criteria for the FOMAS are given. In addition, the above obtained results are further extended to the case where communication delay exists in the control input. Finally, two numerical examples are provided to substantiate the effectiveness of the derived formation criteria.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.