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In this paper, we study formation control problems for vehicle platoons via a neural network-based hybrid impulsive strategy. Specifically, intrinsic nonlinear vehicle dynamics are approximated using the radial basis function neural networks technique (RBFNNs), while overall system stabilization depends on stabilizing control impulses. Based on the Lyapunov-Razumikhin technique, graph theory and LMI method, sufficient stabilization criteria are derived so that tracking errors can be restrained under a compact error bound and vehicle platoon formation control objectives can be further achieved. Lastly, a numerical example is provided to validate the performance and effectiveness of our proposed control strategy.

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Hybrid Impulsive Formation Control of Vehicle Platoons Using Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Zhanlue Liang,
  • Xinzhi Liu

摘要

In this paper, we study formation control problems for vehicle platoons via a neural network-based hybrid impulsive strategy. Specifically, intrinsic nonlinear vehicle dynamics are approximated using the radial basis function neural networks technique (RBFNNs), while overall system stabilization depends on stabilizing control impulses. Based on the Lyapunov-Razumikhin technique, graph theory and LMI method, sufficient stabilization criteria are derived so that tracking errors can be restrained under a compact error bound and vehicle platoon formation control objectives can be further achieved. Lastly, a numerical example is provided to validate the performance and effectiveness of our proposed control strategy.