Composite Antidisturbance H∞ Control for Hidden Markov Jump Systems With Multi-Sensor Against Replay Attacks

• 成果类型: Article
• 署名作者: Wang, Jing; Wang, Dongji; Yan, Huaicheng; Shen, Hao
• 署名单位: Anhui University of Technology; East China University of Science & Technology; Nanjing University of Science & Technology; East China University of Science & Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3326861
• 发表日期: 2024
• 页码: 1760-1766
• 关键词: hidden Markov models; Markov processes; Output feedback; Disturbance observers; detectors; Cyberattack; system performance; Composite antidisturbance control; hidden Markov jump systems; multi-sensor approach; replay attacks

摘要

This article discusses the problem of composite H(infinity )control for hidden Markov jump systems subject to replay attacks. Since it is difficult to obtain the mode information of the system directly in practice, a hidden Markov model is adopted to facilitate subsequent works. The hidden state represents the actual system dynamics that cannot be known exactly, but can be observed by the detector. Considering the multi-disturbance phenomenon, one of which is norm bounded and another is produced by an exogenous system, a composite H-infinity control scheme based on disturbance observer is designed to improve the antidisturbance ability of the system. In addition, with the help of multi-sensor approach, a detection scheme, revealing the attacker's tactics and determining which sensor is assaulted, is presented to withstand replay attacks. Then, a composite disturbance observer-based controller, ensuring that the resulting system is stochastically stable with an expected H-infinity performance under replay attacks, is designed by solving convex optimization problems. Finally, the effectiveness and superiority of the developed method are verified by an example.