{"title":"Distributed Control in Uncertain Nonlinear Multiagent Systems Under Event-Triggered Communication and General Directed Graphs","authors":"Gang Wang;Zongyu Zuo;Peng Li","doi":"10.1109/TSIPN.2024.3422878","DOIUrl":null,"url":null,"abstract":"Designing a consensus algorithm for multiagent systems within an event-triggered communication setting is challenging due to the discontinuous and inaccurate interaction information caused by event-triggering mechanisms. Currently, most related results require an undirected or balanced directed graph. To avoid such restrictive requirements and consider general directed graphs with a spanning tree, we first investigate the perturbed consensus problem of first-order dynamics. Then, we extend our findings to address the consensus problem of the uncertain nonlinear multiagent systems described in Lagrangian dynamics, Brunovsky dynamics, and strict-feedback dynamics under event-triggered communication. We develop three distributed consensus protocols that consider the unique characteristics of these systems and assign different reference signals accordingly. Our proposed schemes ensure that consensus errors either converge to zero or to a small adjustable neighborhood around zero without Zeno behavior while preserving signal boundedness in the closed-loop system. Finally, we conduct extensive simulations to further illustrate the efficiency of our theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"599-609"},"PeriodicalIF":3.0000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10584303/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Designing a consensus algorithm for multiagent systems within an event-triggered communication setting is challenging due to the discontinuous and inaccurate interaction information caused by event-triggering mechanisms. Currently, most related results require an undirected or balanced directed graph. To avoid such restrictive requirements and consider general directed graphs with a spanning tree, we first investigate the perturbed consensus problem of first-order dynamics. Then, we extend our findings to address the consensus problem of the uncertain nonlinear multiagent systems described in Lagrangian dynamics, Brunovsky dynamics, and strict-feedback dynamics under event-triggered communication. We develop three distributed consensus protocols that consider the unique characteristics of these systems and assign different reference signals accordingly. Our proposed schemes ensure that consensus errors either converge to zero or to a small adjustable neighborhood around zero without Zeno behavior while preserving signal boundedness in the closed-loop system. Finally, we conduct extensive simulations to further illustrate the efficiency of our theoretical results.
期刊介绍:
The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.