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<p>This paper investigates the data-driven consensus control problem for multi-agent systems (MASs) under asynchronous Denial-of-Service (DoS) attacks. DoS attacks, particularly those that disrupt communication between agents and their neighbors, severely compromise the validity of MASs consensus control. Existing approaches typically rely on precise system models, which limit their applicability in practical scenarios characterized by unmodeled dynamics. To address these challenges, a novel asynchronous DoS attacks model is developed to capture the disruption behaviors of communication links between agents. Then, dynamic linearization techniques are applied to convert the original nonlinear MASs into a fully form dynamic linearization (FFDL) model, which operates solely on constrained input/output (I/O) data. Moreover, a novel data-driven model-free adaptive resilient control (MFARC) method is proposed to tackle the issue of unavailability of neighbor states caused by DoS attacks. The key of data-driven MFARC method lies in a dynamic compensation mechanism, in which cached historical neighbor states are utilized to generate compensation signals when communication is blocked. Through theoretical analysis, it is proved that the consensus errors remain bounded under the proposed method. Finally, numerical examples validate the effectiveness of the proposed method.</p>

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Data-driven resilient consensus control for nonlinear multi-agent systems under asynchronous DoS attacks

  • 期刊论文

  • 发表日期：

    2026年3月10日

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  • Yan Xie,
  • Lianghao Ji,
  • Xing Guo,
  • Huaqing Li

摘要

This paper investigates the data-driven consensus control problem for multi-agent systems (MASs) under asynchronous Denial-of-Service (DoS) attacks. DoS attacks, particularly those that disrupt communication between agents and their neighbors, severely compromise the validity of MASs consensus control. Existing approaches typically rely on precise system models, which limit their applicability in practical scenarios characterized by unmodeled dynamics. To address these challenges, a novel asynchronous DoS attacks model is developed to capture the disruption behaviors of communication links between agents. Then, dynamic linearization techniques are applied to convert the original nonlinear MASs into a fully form dynamic linearization (FFDL) model, which operates solely on constrained input/output (I/O) data. Moreover, a novel data-driven model-free adaptive resilient control (MFARC) method is proposed to tackle the issue of unavailability of neighbor states caused by DoS attacks. The key of data-driven MFARC method lies in a dynamic compensation mechanism, in which cached historical neighbor states are utilized to generate compensation signals when communication is blocked. Through theoretical analysis, it is proved that the consensus errors remain bounded under the proposed method. Finally, numerical examples validate the effectiveness of the proposed method.