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A neural adaptive output feedback bipartite switching control strategy is proposed for switched nonlinear stochastic multi-agent systems (SMASs) by combining command filter (CF) and dynamic surface control (DSC). The unknown state is reconstructed by the high gain observer, and all the coupling terms and the whole state variables are processed by the properties of radial basis function. The definition of switched SMASs to achieve containment control in probability is clarified, and the detailed proof is given in stability analysis. In addition, based on the common Lyapunov function (CLF) method, it is proved that all system signals remain semi-globally uniformly ultimately bounded (SGUUB) in probability under arbitrary switching.

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Adaptive Bipartite Containment Compensation Control for Nonlinear Stochastic Switched Multi-agent Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Hailin Tang,
  • Xiaolang Tian,
  • Tianping Zhang

摘要

A neural adaptive output feedback bipartite switching control strategy is proposed for switched nonlinear stochastic multi-agent systems (SMASs) by combining command filter (CF) and dynamic surface control (DSC). The unknown state is reconstructed by the high gain observer, and all the coupling terms and the whole state variables are processed by the properties of radial basis function. The definition of switched SMASs to achieve containment control in probability is clarified, and the detailed proof is given in stability analysis. In addition, based on the common Lyapunov function (CLF) method, it is proved that all system signals remain semi-globally uniformly ultimately bounded (SGUUB) in probability under arbitrary switching.