{"title":"利用强化学习实现多领导者异构多代理系统的鲁棒输出组形成跟踪控制","authors":"","doi":"10.1016/j.sysconle.2024.105897","DOIUrl":null,"url":null,"abstract":"<div><p>This paper studies the distributed output formation tracking problem of grouped heterogeneous multi-agent systems under multiple leaders and uncertainties using reinforcement learning (RL). The outputs of followers are supposed to achieve robust tracking to the respective convex point of group leaders while generating an expected time-varying formation configuration. First, a distributed adaptive observer is designed under a directed graph to coordinate the multiple group leaders while estimating the leaders’ dynamics in finite-time. The adaptive mechanism avoids global information of the graph. Second, an optimal tracking problem with respect to the observer is formulated for each follower, while the feedback tracking controller is derived using an action-dependent RL algorithm. An extended learning process for essential dynamics is constructed using the same data, while the output regulation equations are solved equivalently. Third, the robust formation controller and feasibility condition are further proposed based on previous learning results. Stability of the synthetical data-driven controller is analyzed under internal uncertainties and external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the hierarchical control framework.</p></div>","PeriodicalId":49450,"journal":{"name":"Systems & Control Letters","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust output group formation tracking control of heterogeneous multi-agent systems with multiple leaders using reinforcement learning\",\"authors\":\"\",\"doi\":\"10.1016/j.sysconle.2024.105897\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper studies the distributed output formation tracking problem of grouped heterogeneous multi-agent systems under multiple leaders and uncertainties using reinforcement learning (RL). The outputs of followers are supposed to achieve robust tracking to the respective convex point of group leaders while generating an expected time-varying formation configuration. First, a distributed adaptive observer is designed under a directed graph to coordinate the multiple group leaders while estimating the leaders’ dynamics in finite-time. The adaptive mechanism avoids global information of the graph. Second, an optimal tracking problem with respect to the observer is formulated for each follower, while the feedback tracking controller is derived using an action-dependent RL algorithm. An extended learning process for essential dynamics is constructed using the same data, while the output regulation equations are solved equivalently. Third, the robust formation controller and feasibility condition are further proposed based on previous learning results. Stability of the synthetical data-driven controller is analyzed under internal uncertainties and external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the hierarchical control framework.</p></div>\",\"PeriodicalId\":49450,\"journal\":{\"name\":\"Systems & Control Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems & Control Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167691124001853\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems & Control Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167691124001853","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Robust output group formation tracking control of heterogeneous multi-agent systems with multiple leaders using reinforcement learning
This paper studies the distributed output formation tracking problem of grouped heterogeneous multi-agent systems under multiple leaders and uncertainties using reinforcement learning (RL). The outputs of followers are supposed to achieve robust tracking to the respective convex point of group leaders while generating an expected time-varying formation configuration. First, a distributed adaptive observer is designed under a directed graph to coordinate the multiple group leaders while estimating the leaders’ dynamics in finite-time. The adaptive mechanism avoids global information of the graph. Second, an optimal tracking problem with respect to the observer is formulated for each follower, while the feedback tracking controller is derived using an action-dependent RL algorithm. An extended learning process for essential dynamics is constructed using the same data, while the output regulation equations are solved equivalently. Third, the robust formation controller and feasibility condition are further proposed based on previous learning results. Stability of the synthetical data-driven controller is analyzed under internal uncertainties and external disturbances. Finally, simulation results are provided to demonstrate the effectiveness of the hierarchical control framework.
期刊介绍:
Founded in 1981 by two of the pre-eminent control theorists, Roger Brockett and Jan Willems, Systems & Control Letters is one of the leading journals in the field of control theory. The aim of the journal is to allow dissemination of relatively concise but highly original contributions whose high initial quality enables a relatively rapid review process. All aspects of the fields of systems and control are covered, especially mathematically-oriented and theoretical papers that have a clear relevance to engineering, physical and biological sciences, and even economics. Application-oriented papers with sophisticated and rigorous mathematical elements are also welcome.