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This paper investigates formation tracking control of nonlinear multi-agent system (MAS), considering the presence of internal uncertainties and external disturbances. A radial basis function neural network (RBFNN) structure composed of adaptive technology is developed to approximate unknown nonlinear functions, while also providing adaptive estimation techniques to eliminate the effects of state/input-dependent disturbances. Then, a distributed formation control scheme based on representations and estimations is studied and the bounded formation result of the MAS is obtained according to Lyapunov stability theorem. The efficiency of the designed formation control strategy is validated through simulations involving a group of quadrotor aircraft.

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Adaptive Neural Network-Based Anti-Disturbance Formation Control of Multi-agent Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Yuhan Hou,
  • Xiaozheng Jin

摘要

This paper investigates formation tracking control of nonlinear multi-agent system (MAS), considering the presence of internal uncertainties and external disturbances. A radial basis function neural network (RBFNN) structure composed of adaptive technology is developed to approximate unknown nonlinear functions, while also providing adaptive estimation techniques to eliminate the effects of state/input-dependent disturbances. Then, a distributed formation control scheme based on representations and estimations is studied and the bounded formation result of the MAS is obtained according to Lyapunov stability theorem. The efficiency of the designed formation control strategy is validated through simulations involving a group of quadrotor aircraft.