A Novel Asymmetric Functional Approach on Sampled-Data-Based Exponential Consensus of Nonlinear Multi-Agent Systems Against FDI Attacks

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

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-29 DOI:10.1109/TSIPN.2025.3574872

V. M. Janani;K. Subramanian;P. Muthukumar;Hieu Trinh

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引用次数: 0

Abstract

This article investigates the exponential consensus in leaderless multi-agent systems (MASs) subject to Lipschitz nonlinearity, external perturbations, and missing measurements. First, an aperiodic nonfragile sampled-data control strategy is applied to the MASs in the presence of communication delays and randomly occurring false data injection attacks. This protocol provides robustness against controller gain fluctuations and enhances consensus performance with

$H_\infty$

attenuation level. Next, unlike the existing studies, a novel exponential-type asymmetric Lyapunov-Krasovskii functional and a two-sided looped functional are constructed together with the relaxation of positive definiteness for an individual matrix. Utilizing these functionals, exponential consensus conditions are obtained within the form of linear matrix inequalities. Finally, using the YALMIP toolbox in MATLAB, three numerical examples validate theoretical outcomes exhibiting reduced conservatism with improved percentage of performance by maximizing the sampling period with a minimum number of decision variables compared with existing literature.

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基于采样数据的非线性多智能体系统抗FDI攻击指数一致性的一种新的非对称泛函方法

本文研究了受Lipschitz非线性、外部扰动和缺失测量影响的无领导多智能体系统（MASs）中的指数一致性。首先，在存在通信延迟和随机假数据注入攻击的情况下，采用非周期非脆弱采样数据控制策略。该协议提供了对控制器增益波动的鲁棒性，并通过$H_\infty$衰减级别提高了一致性性能。其次，不同于现有的研究，我们构造了一个新的指数型不对称Lyapunov-Krasovskii泛函和一个双面环泛函，并对单个矩阵的正定性进行了松弛。利用这些泛函，得到了线性矩阵不等式形式下的指数一致条件。最后，使用MATLAB中的YALMIP工具箱，三个数值示例验证了与现有文献相比，通过最大化决策变量数量的采样周期，降低了保守性，提高了性能百分比的理论结果。

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.