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In this paper, the optimal formation control problem is addressed for multiple cooperative hypersonic vehicles (HVs) under parameter uncertainties. To provide the position references for HV formation leaders and followers in the presence of switching topologies, the distributed observers are developed. By employing the reinforcement learning theory, data-driven optimal formation control strategies are learned from the interaction between the vehicle system and environment. Furthermore, the obtained optimal strategies can be used to identify the unknown parameters of the HV dynamics in real time. Simulation results of a group of HV is offered to confirm the efficacy of the proposed optimal controller.

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Data-Driven Formation Control of Multiple Uncertain Hypersonic Vehicles

  • 会议论文

  • 发表日期：

    2025年

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  • Ming Cheng,
  • Hao Liu,
  • Qing Gao,
  • Haibin Duan

摘要

In this paper, the optimal formation control problem is addressed for multiple cooperative hypersonic vehicles (HVs) under parameter uncertainties. To provide the position references for HV formation leaders and followers in the presence of switching topologies, the distributed observers are developed. By employing the reinforcement learning theory, data-driven optimal formation control strategies are learned from the interaction between the vehicle system and environment. Furthermore, the obtained optimal strategies can be used to identify the unknown parameters of the HV dynamics in real time. Simulation results of a group of HV is offered to confirm the efficacy of the proposed optimal controller.