{"title":"Adaptive Event-Triggered Output Feedback Consensus Tracking Control of Multi-Agent Systems via K-Filters","authors":"Tianping Zhang, Yanan Duan","doi":"10.1002/rnc.7801","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In this paper, the issue of adaptive event-triggered output feedback consensus tracking dynamic surface control is discussed for nonlinear multi-agent systems (MASs) with unmodeled dynamics. The system states are estimated via K-filters. The unknown nonlinear continuous functions are approximated using radial basis function neural networks (RBFNNs). To lighten the load on communication, an event-triggered control (ETC) method with a relative threshold is developed. By using command filter backstepping technology, a unique adaptive consensus tracking control strategy is presented. Then, through Lyapunov stability analysis, all signals in the closed-loop system can be guaranteed to be semi-globally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon can be avoided. Finally, simulation results validate the effectiveness of the proposed method.</p>\n </div>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"35 6","pages":"2354-2366"},"PeriodicalIF":3.2000,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7801","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, the issue of adaptive event-triggered output feedback consensus tracking dynamic surface control is discussed for nonlinear multi-agent systems (MASs) with unmodeled dynamics. The system states are estimated via K-filters. The unknown nonlinear continuous functions are approximated using radial basis function neural networks (RBFNNs). To lighten the load on communication, an event-triggered control (ETC) method with a relative threshold is developed. By using command filter backstepping technology, a unique adaptive consensus tracking control strategy is presented. Then, through Lyapunov stability analysis, all signals in the closed-loop system can be guaranteed to be semi-globally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon can be avoided. Finally, simulation results validate the effectiveness of the proposed method.
期刊介绍：
Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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