Dynamic Event-Triggering Resilient Coordination for Time-Varying Heterogeneous Networks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-13 DOI:10.1109/TSIPN.2025.3541932

Zirui Liao;Jian Shi;Shaoping Wang;Yuwei Zhang;Rentong Chen;Zhiyong Sun

{"title":"Dynamic Event-Triggering Resilient Coordination for Time-Varying Heterogeneous Networks","authors":"Zirui Liao;Jian Shi;Shaoping Wang;Yuwei Zhang;Rentong Chen;Zhiyong Sun","doi":"10.1109/TSIPN.2025.3541932","DOIUrl":null,"url":null,"abstract":"This study addresses the resilient coordination problem for heterogeneous multi-agent systems (MASs) consisting of first-order and second-order agents in time-invariant and time-varying networks. An internal dynamic variable is introduced to flexibly adjust the triggering threshold and facilitate the dynamic event-triggering condition (DETC). Under adversarial attacks, a novel resilient consensus strategy called <italic>heterogeneous dynamic event-triggering mean-subsequence-reduced (HDE-MSR) algorithm</i> is further developed, which ensures that the positions of all healthy agents achieve consensus on the identical value and the velocities of all healthy second-order agents asymptotically approach zero despite the influence of faulty agents. Moreover, the resilient consensus in time-varying networks is further guaranteed by the introduction of jointly robust graphs. Finally, three case studies are provided to validate the effectiveness and superior performance of the HDE-MSR algorithm.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"329-341"},"PeriodicalIF":3.0000,"publicationDate":"2025-02-13","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/10884930/","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

This study addresses the resilient coordination problem for heterogeneous multi-agent systems (MASs) consisting of first-order and second-order agents in time-invariant and time-varying networks. An internal dynamic variable is introduced to flexibly adjust the triggering threshold and facilitate the dynamic event-triggering condition (DETC). Under adversarial attacks, a novel resilient consensus strategy called heterogeneous dynamic event-triggering mean-subsequence-reduced (HDE-MSR) algorithm is further developed, which ensures that the positions of all healthy agents achieve consensus on the identical value and the velocities of all healthy second-order agents asymptotically approach zero despite the influence of faulty agents. Moreover, the resilient consensus in time-varying networks is further guaranteed by the introduction of jointly robust graphs. Finally, three case studies are provided to validate the effectiveness and superior performance of the HDE-MSR algorithm.

查看原文本刊更多论文

时变异构网络的动态事件触发弹性协调

本文研究了时变和定常网络中由一阶和二阶智能体组成的异构多智能体系统（MASs）的弹性协调问题。引入内部动态变量，灵活调整触发阈值，方便动态事件触发条件（DETC）。在对抗性攻击下，进一步提出了一种新的弹性一致策略——异构动态事件触发平均子序列减少（HDE-MSR）算法，该算法保证所有健康智能体的位置在相同的值上达成一致，并保证所有健康二阶智能体的速度在不受故障智能体影响的情况下渐近于零。通过引入联合鲁棒图，进一步保证时变网络的弹性一致。最后，通过三个实例验证了HDE-MSR算法的有效性和优越性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

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.