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HMM-based adaptive attack-resilient control for Markov jump system and application to an aircraft model

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  • He, Hangfeng
  • Qi, Wenhai
  • Kao, Yonggui

Abstract

This paper deals with adaptive attack-resilient control for Markov jump systems, in which the system mode, sensor and actuator are all attacked. Not only the inputs of system and controller are tampered, but the system mode is also tampered, which may trigger asynchronous switching phenomenon. To describe the asynchronous switching phenomenon, the closed-loop system is modeled as a hidden markov model (HMM). The adaptive control technique is applied to deal with the time-varying attacks on sensor and actuator with reasonable constraints. With the proposed HMM-based adaptive attack-resilient control strategy, the states of Markov system and parameter estimation errors are bounded and the corresponding upper bounds are analyzed. Finally, the proposed strategy is applied to an aircraft model.

Suggested Citation

  • He, Hangfeng & Qi, Wenhai & Kao, Yonggui, 2021. "HMM-based adaptive attack-resilient control for Markov jump system and application to an aircraft model," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    • Handle: RePEc:eee:apmaco:v:392:y:2021:i:c:s0096300320306214
    DOI: 10.1016/j.amc.2020.125668
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    References listed on IDEAS

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    1. Xing, Mengping & Xia, Jianwei & Wang, Jing & Meng, Bo & Shen, Hao, 2019. "Asynchronous H∞ filtering for nonlinear persistent dwell-time switched singular systems with measurement quantization," Applied Mathematics and Computation, Elsevier, vol. 362(C), pages 1-1.
    2. Song, Xiaona & Men, Yunzhe & Zhou, Jianping & Zhao, Junjie & Shen, Hao, 2017. "Event-triggered H∞ control for networked discrete-time Markov jump systems with repeated scalar nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 298(C), pages 123-132.
    3. Xu, Zhaowen & Su, Hongye & Shi, Peng & Wu, Zheng-Guang, 2019. "Asynchronous H∞ control of semi-Markov jump linear systems," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 270-280.
    4. Li, Feng & Song, Shuai & Zhao, Jianrong & Xu, Shengyuan & Zhang, Zhengqiang, 2019. "Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 1-13.
    5. Yingxue Hou & Shaocheng Tong & Yongming Li, 2016. "Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(15), pages 3581-3590, November.
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    Cited by:

    1. Wang, Yun & Fang, Tian & Kong, Qingkai & Li, Feng, 2024. "Zero-sum game-based optimal control for discrete-time Markov jump systems: A parallel off-policy Q-learning method," Applied Mathematics and Computation, Elsevier, vol. 467(C).
    2. Wang, Lanxin & Long, Yue & Li, Tieshan & Yang, Hanqing & Philip Chen, C.L., 2024. "Asynchronous attack tolerant control for Markov jump cyber-physical systems under hybrid cyber-attacks," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    3. Yang Li & Taitao Feng & Yaolei Wang & Yifei Xin, 2021. "A High Order Accurate and Effective Scheme for Solving Markovian Switching Stochastic Models," Mathematics, MDPI, vol. 9(6), pages 1-15, March.
    4. Shuguang Liu & Yueyuan Zhang & Yuan Sun, 2024. "The Event-Triggered Resilient Control of Discrete-Time Nonlinear Semi-Markov Jump Systems Based on Incremental Quadratic Constraints," Mathematics, MDPI, vol. 12(14), pages 1-16, July.
    5. Zhang, Ning & Qi, Wenhai & Pang, Guocheng & Cheng, Jun & Shi, Kaibo, 2022. "Observer-based sliding mode control for fuzzy stochastic switching systems with deception attacks," Applied Mathematics and Computation, Elsevier, vol. 427(C).

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