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<p>This paper investigates event-triggered practical predefined-time consensus for stochastic nonlinear multi-agent systems (MASs) with uncertain dynamics. To reduce the computational burden, an event-triggered mechanism is employed, and Zeno behavior is rigorously excluded through theoretical analysis. By employing fuzzy logic systems (FLS) to approximate the unknown nonlinear dynamics and constructing an adaptive controller based on the backstepping technique, the stochastic MASs achieve practically predefined-time stochastic stabilization (PPSS). The stabilization time is explicitly determined by a single design parameter, which enables the convergence time to be set as desired. The proposed control strategy ensures that all closed-loop signals remain bounded and that predefined-time consensus is achieved, which is defined by the accurate output tracking of the follower agents to the leader. Lastly, two simulations are provided to validate the effectiveness and feasibility of the theoretical framework.</p>

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Event-triggered predefined-time adaptive consensus control for stochastic multi-agent systems

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Guangzhi Wang,
  • Tianliang Zhang,
  • Xiushan Jiang,
  • Weihai Zhang

摘要

This paper investigates event-triggered practical predefined-time consensus for stochastic nonlinear multi-agent systems (MASs) with uncertain dynamics. To reduce the computational burden, an event-triggered mechanism is employed, and Zeno behavior is rigorously excluded through theoretical analysis. By employing fuzzy logic systems (FLS) to approximate the unknown nonlinear dynamics and constructing an adaptive controller based on the backstepping technique, the stochastic MASs achieve practically predefined-time stochastic stabilization (PPSS). The stabilization time is explicitly determined by a single design parameter, which enables the convergence time to be set as desired. The proposed control strategy ensures that all closed-loop signals remain bounded and that predefined-time consensus is achieved, which is defined by the accurate output tracking of the follower agents to the leader. Lastly, two simulations are provided to validate the effectiveness and feasibility of the theoretical framework.