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Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method

Author

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  • Xiao, Junwen
  • Liu, Yongchao

Abstract

This paper presents a self-triggered consensus resilient control method for nonlinear multi-agent systems (MASs) under sensor deception attacks. A single parameter learning method is integrated into backstepping technique to simplify design procedure. The neural networks are utilized to compensate for unknown dynamics of the MASs. Moreover, a self-triggered mechanism is presented for MASs to refrain from continuously monitoring triggering conditions and conserve communication resources. The designed controller can resist sensor deception attacks and guarantee that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.

Suggested Citation

  • Xiao, Junwen & Liu, Yongchao, 2024. "Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    • Handle: RePEc:eee:chsofr:v:189:y:2024:i:p1:s0960077924012013
    DOI: 10.1016/j.chaos.2024.115649
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    References listed on IDEAS

    as
    1. Ma, Yajing & Li, Zhanjie & Xie, Xiangpeng & Yue, Dong, 2023. "Adaptive consensus of uncertain switched nonlinear multi-agent systems under sensor deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. Jiang, Xiaowei & Zhang, Ni & Li, Bo & Zhang, Xianhe, 2023. "Impulsive consensus of nonlinear multi-agent systems with input and state saturation constraints," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
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