{"title":"异构线性多代理网络的分布式学习控制","authors":"","doi":"10.1016/j.automatica.2024.111838","DOIUrl":null,"url":null,"abstract":"<div><p>This paper deals with cooperative output tracking problems for heterogeneous networks of linear agents. To refine high-precision tracking performances of agents, a graph-based distributed learning control (DLC) law is proposed, for which a new bounded-initialization, bounded-updating (BIBU) stability property is explored under any bounded initial conditions. Moreover, a class of heterogeneous-to-homogeneous transformation methods is introduced, together with presenting feasible gain design conditions, for DLC. It is shown that with the designed DLC law, not only can the effect of the agents’ heterogeneous dynamics in performing DLC be well overcome, but also the BIBU stability and the robust cooperative output tracking of agents can be simultaneously accomplished. A simulation test is also implemented to verify the validity of our developed DLC results for heterogeneous vehicle networks.</p></div>","PeriodicalId":55413,"journal":{"name":"Automatica","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distributed learning control for heterogeneous linear multi-agent networks\",\"authors\":\"\",\"doi\":\"10.1016/j.automatica.2024.111838\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper deals with cooperative output tracking problems for heterogeneous networks of linear agents. To refine high-precision tracking performances of agents, a graph-based distributed learning control (DLC) law is proposed, for which a new bounded-initialization, bounded-updating (BIBU) stability property is explored under any bounded initial conditions. Moreover, a class of heterogeneous-to-homogeneous transformation methods is introduced, together with presenting feasible gain design conditions, for DLC. It is shown that with the designed DLC law, not only can the effect of the agents’ heterogeneous dynamics in performing DLC be well overcome, but also the BIBU stability and the robust cooperative output tracking of agents can be simultaneously accomplished. A simulation test is also implemented to verify the validity of our developed DLC results for heterogeneous vehicle networks.</p></div>\",\"PeriodicalId\":55413,\"journal\":{\"name\":\"Automatica\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automatica\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0005109824003327\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automatica","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0005109824003327","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Distributed learning control for heterogeneous linear multi-agent networks
This paper deals with cooperative output tracking problems for heterogeneous networks of linear agents. To refine high-precision tracking performances of agents, a graph-based distributed learning control (DLC) law is proposed, for which a new bounded-initialization, bounded-updating (BIBU) stability property is explored under any bounded initial conditions. Moreover, a class of heterogeneous-to-homogeneous transformation methods is introduced, together with presenting feasible gain design conditions, for DLC. It is shown that with the designed DLC law, not only can the effect of the agents’ heterogeneous dynamics in performing DLC be well overcome, but also the BIBU stability and the robust cooperative output tracking of agents can be simultaneously accomplished. A simulation test is also implemented to verify the validity of our developed DLC results for heterogeneous vehicle networks.
期刊介绍:
Automatica is a leading archival publication in the field of systems and control. The field encompasses today a broad set of areas and topics, and is thriving not only within itself but also in terms of its impact on other fields, such as communications, computers, biology, energy and economics. Since its inception in 1963, Automatica has kept abreast with the evolution of the field over the years, and has emerged as a leading publication driving the trends in the field.
After being founded in 1963, Automatica became a journal of the International Federation of Automatic Control (IFAC) in 1969. It features a characteristic blend of theoretical and applied papers of archival, lasting value, reporting cutting edge research results by authors across the globe. It features articles in distinct categories, including regular, brief and survey papers, technical communiqués, correspondence items, as well as reviews on published books of interest to the readership. It occasionally publishes special issues on emerging new topics or established mature topics of interest to a broad audience.
Automatica solicits original high-quality contributions in all the categories listed above, and in all areas of systems and control interpreted in a broad sense and evolving constantly. They may be submitted directly to a subject editor or to the Editor-in-Chief if not sure about the subject area. Editorial procedures in place assure careful, fair, and prompt handling of all submitted articles. Accepted papers appear in the journal in the shortest time feasible given production time constraints.