基 于 逆 强 化 学 习 的 航 天 器 交 会 对 接 方 法

Chenglei Yue, Xuechuan Wang, Xiaokui Yue, Ting Song

科研成果: 期刊稿件文章同行评审

2 引用 (Scopus)

摘要

For spacecraft proximity maneuvering and rendezvous, a method for training neural networks based on generative adversarial inverse reinforcement learning is proposed by using model predictive control to provide the ex- pert dataset. Firstly, considering the maximum velocity constraint, the control input saturation constraint and the space cone constraint, the dynamics of the chaser spacecraft approaching a static target is established. Then, the chaser spacecraft is driven to reach the target using model predictive control. Secondly, disturbances are added to the nomi- nal trajectory, and the trajectories from each starting positions to the target are calculated using the aforementioned method. The state and command of trajectories at each time are collected to form a training set. Finally, the network structure and parameters are set, and hyperparameters are trained. Driven by the training set, the adversarial inverse reinforcement learning method is used to train the network. The simulation results show that adversarial inverse rein- forcement learning can imitate the behavior of expert trajectories, and successfully train the neural network to drive the spacecraft to move from the starting point to the static target.

投稿的翻译标题A spacecraft rendezvous and docking method based on inverse reinforcement learning
源语言繁体中文
文章编号328420
期刊Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
44
19
DOI
出版状态已出版 - 15 10月 2023

关键词

  • generative adversarial inverse reinforcement learning
  • imitation learning
  • model predictive control
  • network training
  • neural network

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