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Event-triggered adaptive secure tracking control for nonlinear cyber–physical systems against unknown deception attacks

Author

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  • Tian, Yongjie
  • Zhang, Huiyan
  • Liu, Yongchao
  • Zhao, Ning
  • Mathiyalagan, Kalidass

Abstract

This article presents an event-triggered adaptive neural networks secure tracking control method for a class of nonlinear cyber–physical systems under unknown sensor and actuator deception attacks. To obtain the desired system performance, dynamic surface technique is applied to design controller and radial basis function neural networks are introduced to deal with unknown nonlinear and actuator attacks. By skillfully combining compensation signals with the attack compensators, the unknown deception attacks are effectively mitigated. To reduce the transmission communication load, a novel event-driven control scheme is developed by applying relative threshold triggered mechanism. On this basis, all the signals of closed-loop system are bounded under deception attacks by using Lyapunov stability analysis. Additionally, the presented secure tracking control strategy can ensure the tracking error converges to a small neighborhood of origin and the Zeno behavior is ruled out. Finally, a practical example is employed to verify the feasibility and effectiveness of the designed control algorithm.

Suggested Citation

  • Tian, Yongjie & Zhang, Huiyan & Liu, Yongchao & Zhao, Ning & Mathiyalagan, Kalidass, 2024. "Event-triggered adaptive secure tracking control for nonlinear cyber–physical systems against unknown deception attacks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 221(C), pages 79-93.
    • Handle: RePEc:eee:matcom:v:221:y:2024:i:c:p:79-93
    DOI: 10.1016/j.matcom.2024.02.022
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    References listed on IDEAS

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    1. Wenhui Liu & Cheng-Chew Lim & Shengyuan Xu, 2017. "Adaptive control of a class of quantised nonlinearly parameterised systems with unknown control directions," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(5), pages 941-951, April.
    2. Liu, Xiaohua & Li, Mengling & Zeng, Pengyu, 2024. "Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 82-97.
    3. Huang, Jingyang & Jia, Wei & Wan, Tongxin & Xiao, Shuyi & Wang, Liqi & Dong, Jiuxiang, 2023. "Adaptive event-triggered fault-tolerant consensus of linear heterogeneous multiagent systems via hierarchical approach," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    4. Zhao, Jipeng & Yang, Guang-Hong, 2023. "Fuzzy adaptive secure tracking control against unknown false data injection attacks for uncertain nonlinear systems with input quantization," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    5. Tansel Yucelen & Wassim M. Haddad & Eric M. Feron, 2016. "Adaptive control architectures for mitigating sensor attacks in cyber-physical systems," Cyber-Physical Systems, Taylor & Francis Journals, vol. 2(1-4), pages 24-52, October.
    6. Shen, Zhihao & Zhang, Liang & Niu, Ben & Zhao, Ning, 2023. "Event-based reachable set synthesis for delayed nonlinear semi-Markov systems," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
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