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A Robust Dynamic Compensation Approach for Cyber-Physical Systems Against Multiple Types of Actuator Attacks

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  • Huang, Xin
  • Dong, Jiuxiang

Abstract

This paper develops a new secure control scheme (SCS) with a robust dynamic compensation for cyber-physical systems (CPSs) against malicious actuator attacks. The attack model under consideration is assumed to be a Lipschitz continuous and derivable function with respect to CPS states and an unknown bounded signal. The bounded signal is generated from any linear/nonlinear finite-L2-gain dynamical system. Different from the existing SCSs on one given injection attack pattern, (a) the new one can simultaneously resist multiple types of attacks (e.g., dynamic attacks, state-dependent attacks, etc.); (b) with the help of cooperation between two virtual dynamical systems, the presented robust compensator achieves a better compensation to attacks, such that the proposed control scheme guarantees the asymptotical stability of the attacked systems. An illustrative example verifies the effectiveness of the presented method.

Suggested Citation

  • Huang, Xin & Dong, Jiuxiang, 2020. "A Robust Dynamic Compensation Approach for Cyber-Physical Systems Against Multiple Types of Actuator Attacks," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:apmaco:v:380:y:2020:i:c:s0096300320302538
    DOI: 10.1016/j.amc.2020.125284
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    References listed on IDEAS

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    1. Gang Wang & Chaoli Wang & Yan Yan & Lin Li & Xuan Cai, 2017. "Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(3), pages 587-603, February.
    2. Liu, Jinliang & Xia, Jilei & Tian, Engang & Fei, Shumin, 2018. "Hybrid-driven-based H∞ filter design for neural networks subject to deception attacks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 158-174.
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    Cited by:

    1. Song, Yunbo & Ye, Dan, 2021. "Optimizing DoS attack energy with imperfect acknowledgments and energy harvesting constraints in cyber-physical systems," Applied Mathematics and Computation, Elsevier, vol. 393(C).
    2. Gao, Rui & Yang, Guang-Hong, 2022. "Sampled-data distributed state estimation with multiple transmission channels under denial-of-service attacks," Applied Mathematics and Computation, Elsevier, vol. 429(C).
    3. Oliveira, Pedro M. & Palma, Jonathan M. & Lacerda, Márcio J., 2022. "H2 state-feedback control for discrete-time cyber-physical uncertain systems under DoS attacks," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    4. Li, Lei & Wang, Wenting & Ma, Qiang & Pan, Kunpeng & Liu, Xin & Lin, Lin & Li, Jian, 2021. "Cyber attack estimation and detection for cyber-physical power systems," Applied Mathematics and Computation, Elsevier, vol. 400(C).
    5. Lü, Shao-Yu & Jin, Xiao-Zheng & Wang, Hai & Deng, Chao, 2021. "Robust adaptive estimation and tracking control for perturbed cyber-physical systems against denial of service," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    6. Su, Qingyu & Wang, Handong & Sun, Chaowei & Li, Bo & Li, Jian, 2022. "Cyber-attacks against cyber-physical power systems security: State estimation, attacks reconstruction and defense strategy," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    7. Sun, Haibin & Cui, Yahui & Hou, Linlin & Shi, Kaibo, 2022. "Adaptive finite-time control for cyber-physical systems with injection and deception attacks," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    8. Hou, Linlin & Li, Yao & Luo, Wende & Sun, Haibin, 2022. "Adaptive tracking control of switched cyber-physical systems with cyberattacks," Applied Mathematics and Computation, Elsevier, vol. 415(C).

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