Leader-Following Consensus Control of Unknown Nonlinear MASs Under False Data Injection Attacks

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-29 DOI:10.1109/TNSE.2024.3433392

Meirong Wang;Jianqiang Hu;Ahmed Alsaedi;Jinde Cao

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Abstract

This paper studies the distributed leader-following consensus problem of unknown nonlinear multi-agent systems (MASs) under false data injection attacks (FDIAs), where the followers connected to the leader may receive the injected false data from the leader's communication channels. Due to the existence of FDIAs, the real and broken leader state value is not available to the followers and cannot be used by followers' controllers, thus an attack compensator based on the errors between the predictive value and the actual measured value is added to the controller to mitigate the adverse effects of attacks. Fuzzy logic systems (FLSs) and Neural Network (NN) techniques are applied to approximate the unknown nonlinear dynamic by estimating the weight matrix. The proposed controller combines attack compensation with unknown nonlinear function compensation, and finally obtains sufficient conditions for the MASs to be ultimately uniformly bounded (UUB). Two algorithms are presented for undirected and directed communication topologies respectively and the simulation results verify the feasibility of the proposed consensus algorithms.

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虚假数据注入攻击下未知非线性 MAS 的领导者追随共识控制

本文研究了未知非线性多Agent系统（MAS）在虚假数据注入攻击（FDIAs）下的分布式领导者-跟随者共识问题，在这种情况下，与领导者相连的跟随者可能会从领导者的通信信道接收到注入的虚假数据。由于 FDIAs 的存在，跟随者无法获得真实的、被破坏的领导者状态值，跟随者的控制器也无法使用，因此需要在控制器中加入一个基于预测值与实际测量值之间误差的攻击补偿器，以减轻攻击的不利影响。模糊逻辑系统（FLS）和神经网络（NN）技术通过估计权重矩阵来近似未知的非线性动态。所提出的控制器将攻击补偿与未知非线性函数补偿相结合，最终获得了 MAS 最终均匀有界（UUB）的充分条件。针对无向和有向通信拓扑分别提出了两种算法，仿真结果验证了所提共识算法的可行性。

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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.