Composite-Observer-Based Adaptive Consensus Tracking Control for Nonlinear MASs With Unknown Control Directions Against Deception Attacks

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Luyao Wen;Ben Niu;Xudong Zhao;Guangdeng Zong;Ding Wang;Wencheng Wang;Yuqiang Jiang
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引用次数: 0

Abstract

This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader’s output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.
基于复合观测器的非线性 MAS 自适应共识跟踪控制,可对抗未知控制方向的欺骗攻击
本文主要研究了控制方向未知的非线性多智能体系统在欺骗攻击下的自适应输出反馈一致跟踪控制问题。首先,建立了状态观测器和干扰观测器的复合观测器,用于同时估计面对欺骗攻击和不可测干扰的状态。此外,为了解决欺骗攻击带来的未知增益,提出了自适应攻击补偿器。在此基础上,结合设计最后阶段的对数Lyapunov函数和智能逼近技术,构造了一种新的基于复合观测器的自适应一致跟踪控制策略。所提出的控制策略既保证了所有闭环信号的有界性,又实现了follower对leader输出的同步跟踪。最后,通过两个仿真实例验证了所提控制策略的有效性。
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来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
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