Event-Triggered Bipartite Consensus for Multi-Agent Systems via Model-Free Sliding-Mode Scheme

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-12-30 DOI:10.1109/TNSE.2024.3524383

Huarong Zhao;Li Peng;Linbo Xie;Hongnian Yu

引用次数: 0

Abstract

This paper addresses the challenge of achieving fully distributed event-triggered bipartite consensus in discrete-time nonlinear multi-agent systems (MASs) characterized by unknown dynamic models and antagonistic interactions. It begins by transforming the bipartite consensus issue into a standard consensus problem through a combined measurement error function. A dynamic linearization model is subsequently established for the input and the combined measurement error function, easing the strongly connected requirement of MASs' communication topology. To enhance performance, an event-triggered model-free sliding-mode bipartite consensus algorithm is proposed, designed to boost convergence speed, reduce steady-state error, and relieve communication burden. The convergence of the proposed method is rigorously proven, allowing for control of fine-tuned specific practical needs. Simulation studies are conducted to verify the effectiveness of the proposed scheme.

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.