Consensus Analysis for Cooperative-Competitive Multiagent Systems Under False Data Injection Attacks via Dynamic Event-Triggered Observers

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

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-19 DOI:10.1109/TSIPN.2024.3375611

Sangli Shi;Zhengxin Wang;Min Xiao;Guo-Ping Jiang;Jinde Cao

引用次数: 0

Abstract

Distributed secure control is investigated for cooperative-competitive multiagent systems suffered from false data injection attacks (FDIAs) via event-triggered observers. Attack signals are injected into controller-to-actuator channels. A static event-triggered control is first presented, then an auxiliary-variable-based dynamic event-triggered control is further put forward. The dynamic event-triggered control ensures fewer triggering instants and the dynamic variable plays a significant part in the exclusion of Zeno-behavior. Then based on estimated states and attacks calculated by observers, distributed controllers are proposed to resist attacks. Bipartite consensus is ensured in multiagent systems and corresponding sufficient conditions are obtained. Meanwhile, the Zeno-behaviors are proven to be nonexistent. Finally, theoretical analyses are explained by simulations.

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通过动态事件触发观测器对虚假数据注入攻击下的合作竞争型多代理系统进行共识分析

通过事件触发观测器，研究了遭受虚假数据注入攻击（FDIAs）的合作竞争多代理系统的分布式安全控制。攻击信号被注入控制器到执行器的通道。首先提出了一种静态事件触发控制，然后进一步提出了一种基于辅助变量的动态事件触发控制。动态事件触发控制确保了更少的触发时刻，而且动态变量在排除 Zeno- 行为方面发挥了重要作用。然后，基于观测器计算出的估计状态和攻击，提出了抵御攻击的分布式控制器。确保了多代理系统中的两方共识，并得到了相应的充分条件。同时，Zeno 行为被证明是不存在的。最后，通过模拟解释了理论分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.