Composite Anti-Disturbance Control for Nonlinear Hidden Markov Jump Systems Under Replay Attacks: A Dynamic Output-Feedback Method

IF 9.4 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-10-01 DOI:10.1109/TCYB.2024.3455935
Dongji Wang;Shengyuan Xu
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Abstract

This article is dedicated to researching the problem of composite anti-disturbance control for nonlinear hidden Markov jump systems, described by the interval type-2 Takagi-Sugeno fuzzy method, under replay attacks in the continuous-time domain. Based on dynamic output feedback control and disturbance observer, a fuzzy composite controller, following the core idea of nonparallel distribution compensation, is designed to compensate for the impact of multiple disturbances. For replay attacks, a multisensor scheme with the detection mechanism is introduced, enabling the studied systems to remain stable even under attacks. Afterward, with the aid of the Lyapunov stability theory, sufficient conditions for ensuring the stability of the resulting systems are deduced, and then the gains of the desired controller and disturbance observer are obtained. Finally, the rationality and effectiveness of the established method are demonstrated through simulation, as well as its superiority over the traditional ${\mathcal {H}}_{infty }$ control method.
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重放攻击下非线性隐马尔可夫跳跃系统的复合抗干扰控制:一种动态输出反馈方法
本文致力于研究连续时域重放攻击下,用区间-2型Takagi-Sugeno模糊方法描述的非线性隐马尔可夫跃迁系统的复合抗干扰控制问题。基于动态输出反馈控制和扰动观测器,设计了一种模糊复合控制器,遵循非平行分布补偿的核心思想,以补偿多重扰动的影响。针对重放攻击,引入了具有检测机制的多传感器方案,使所研究的系统即使在攻击下也能保持稳定。随后,借助李雅普诺夫稳定性理论,推导出确保所研究系统稳定性的充分条件,进而得到所需的控制器和扰动观测器的增益。最后,通过仿真证明了所建立方法的合理性和有效性,以及与传统 ${mathcal {H}}_{infty }$ 控制方法相比的优越性。
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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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