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A resilient optimized dynamic event-triggered mechanism on networked control system with switching behavior under mixed attacks

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  • Ma, Yan
  • Zhang, Zhenzhen
  • Yang, Li
  • Chen, Hao
  • Zhang, Yihao

Abstract

This paper studies the stability problem and H∞ performance of networked control system (NCS) in the presence of mixed attacks by developing an improved event-triggered mechanism (ETM). Firstly, both aperiodic denial of service (DoS) attacks and random deception attacks are encountered for investigating system dynamic behavior. Then, a resilient optimized dynamic event-triggered mechanism (RODETM) is proposed for reducing the unnecessary costs of system operation and mitigating the impact caused by attacks. On this basis, the standard NCS is reformulated into a switched system under the mixed attacks. Further, using the piecewise Lyapunov-Krasovskii functional method, average dwell time scheme and the linear matrix inequality (LMI) method, some sufficient conditions with control design implementation are obtained, which can ensure the exponential stability with the expected prescribed H∞ performance index of the studied system. Finally, the effectiveness of the proposed approach is demonstrated by using a numerical simulation with comparative analysis.

Suggested Citation

  • Ma, Yan & Zhang, Zhenzhen & Yang, Li & Chen, Hao & Zhang, Yihao, 2022. "A resilient optimized dynamic event-triggered mechanism on networked control system with switching behavior under mixed attacks," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    • Handle: RePEc:eee:apmaco:v:430:y:2022:i:c:s0096300322003745
    DOI: 10.1016/j.amc.2022.127300
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    References listed on IDEAS

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    1. Feng, Zongying & Shao, Hanyong & Shao, Lin, 2020. "Further results on event-triggered H∞ networked control for neural networks with stochastic cyber-attacks," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    2. Chang, Xiao-Heng & Jin, Xue, 2022. "Observer-based fuzzy feedback control for nonlinear systems subject to transmission signal quantization," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    3. Zhang, Ruimei & Zeng, Deqiang & Zhong, Shouming & Yu, Yongbin, 2017. "Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 57-74.
    4. Wang, Yingchun & Zheng, Yu & Xie, Xiangpeng & Yang, Jun, 2020. "An improved reduction method based networked control against false data injection attacks and stochastic input delay," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    5. 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.
    6. Chang, Beibei & Mu, Xiaowu & Yang, Zhe & Fang, Jianyin, 2021. "Event-based secure consensus of muti-agent systems under asynchronous DoS attacks," Applied Mathematics and Computation, Elsevier, vol. 401(C).
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    Cited by:

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