Distributed Model-Free Adaptive Predictive Control for MIMO Multi-Agent Systems With Deception Attack

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

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-01-01 DOI:10.1109/TSIPN.2023.3346994

Zhenzhen Pan;Ronghu Chi;Zhongsheng Hou

引用次数: 0

Abstract

This work explores the challenging problems of nonlinear dynamics, nonaffine structures, heterogeneous properties, and deception attack together and proposes a novel distributed model-free adaptive predictive control (DMFAPC) for multiple-input-multiple-output (MIMO) multi-agent systems (MASs). A dynamic linearization method is introduced to address the nonlinear heterogeneous dynamics which is transformed as the unknown parameters in the obtained linear data model. A radial basis function neural network is designed to detect the deception attack and to estimate the polluted output that is further used in the controller design to compensate for the effect. Then, the DMFAPC is designed by defining a new expanded distributed output with a stochastic factor introduced. The bounded convergence is proved by using the contraction mapping method and the effectiveness of the proposed DMFAPC is verified by simulation examples.

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具有欺骗攻击的多输入多输出多代理系统的分布式无模型自适应预测控制

这项研究探讨了非线性动力学、非石蜡结构、异质特性和欺骗攻击等具有挑战性的问题，并为多输入多输出（MIMO）多代理系统（MASs）提出了一种新型分布式无模型自适应预测控制（DMFAPC）。引入了一种动态线性化方法来解决非线性异构动态问题，该方法将非线性异构动态转化为所获得的线性数据模型中的未知参数。设计了一个径向基函数神经网络来检测欺骗攻击，并估算污染输出，进一步用于控制器设计以补偿该影响。然后，通过引入随机因素定义新的扩展分布式输出来设计 DMFAPC。利用收缩映射法证明了有界收敛性，并通过仿真实例验证了所提出的 DMFAPC 的有效性。

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.