跳到主要导航 跳到搜索 跳到主要内容

Coordinated Decision-Making of Heading and Morphing for a Morphing Reusable Launch Vehicle via Multi-Agent Reinforcement Learning

  • Baochao Zhang
  • , Haoning Wang
  • , Jie Guo*
  • , Yangyang Wan
  • , Yan Xiang
  • , Shengjing Tang
  • *此作品的通讯作者
  • Beijing Institute of Technology
  • Beijing Institute of Astronautical Systems Engineering

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The application of single agent reinforcement learning faces significant challenges in coordinated decision-making of heading and morphing for morphing reusable launch vehicles. By applying mutli-agent reinforcement learning, this paper proposes a coordinated decision-making method that is capable of determining heading and morphing command simultaneously. Firstly, the coordinated decision-making framework is established specifically for the morphing reusable launch vehicle. The heading and morphing decision-making agents are designed to execute the mission of reaching designated location and avoiding no-fly zones along trajectory. A multi-stage training strategy is developed to accelerate the training process. Simulation results show the effectiveness and superiority of the proposed method over the coordinated decision-making method based on single agent reinforcement learning. Compared with the fixed-wing reusable launch vehicle, the morphing reusable launch vehicle with the proposed method performs less number of bank angle reversal.

源语言英语
主期刊名Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025
出版商Institute of Electrical and Electronics Engineers Inc.
5305-5311
页数7
ISBN(电子版)9798331510565
DOI
出版状态已出版 - 2025
已对外发布
活动37th Chinese Control and Decision Conference, CCDC 2025 - Xiamen, 中国
期限: 16 5月 202519 5月 2025

出版系列

姓名Proceedings of the 37th Chinese Control and Decision Conference, CCDC 2025

会议

会议37th Chinese Control and Decision Conference, CCDC 2025
国家/地区中国
Xiamen
时期16/05/2519/05/25

指纹

探究 'Coordinated Decision-Making of Heading and Morphing for a Morphing Reusable Launch Vehicle via Multi-Agent Reinforcement Learning' 的科研主题。它们共同构成独一无二的指纹。

引用此