| 引用本文: | 张乘铭,朱彦伟,杨乐平,等.航天器交会型轨道追逃策略的滚动时域优化.[J].国防科技大学学报,2024,46(3):21-29.[点击复制] |
| ZHANG Chengming,ZHU Yanwei,YANG Leping,et al.Receding horizon optimization for spacecraft pursuit-evasion strategy in rendezvous[J].Journal of National University of Defense Technology,2024,46(3):21-29[点击复制] |
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| 航天器交会型轨道追逃策略的滚动时域优化 |
| 张乘铭,朱彦伟,杨乐平,杨傅云翔 |
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(国防科技大学 空天科学学院, 湖南 长沙 410073)
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| 摘要: |
| 针对航天器自由时域交会型轨道追逃过程中的测量误差等不确定性对交会的影响,提出了一种基于滚动时域优化的高时效策略求解方法。根据微分对策理论推导得到博弈鞍点控制策略,并对问题进行等价转换；通过预先离线求解开环鞍点策略,将问题初始状态和相应的解作为样本以进行神经网络训练,训练后的网络结构可以快速得到相应问题的近似解。为了更好地应对博弈环境中的测量噪声,基于神经网络结构设计了滚动时域求解框架,并通过周期性的滚动求解最终实现对逃逸航天器的交会。数值仿真表明,所提出的策略可以有效应对测量噪声不确定性,且相比于文献中已有的策略,计算耗时可从分钟级降至秒级。 |
| 关键词: 追逃博弈 微分对策 深度神经网络 滚动时域优化 |
| DOI：10.11887/j.cn.202403003 |
| 投稿日期：2023-03-14 |
| 基金项目:国防科技大学自主创新科学基金资助项目(22-ZZCX-083) |
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| Receding horizon optimization for spacecraft pursuit-evasion strategy in rendezvous |
| ZHANG Chengming, ZHU Yanwei, YANG Leping, YANG Fuyunxiang |
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(College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| Given the influence of uncertainty such as measurement errors in the process of spacecraft free-time orbital pursuit-evasion game for rendezvous, a high-efficiency strategy based on receding horizon optimization was proposed as a solution method. The saddle point control strategy of the game was derived according to differential games, and the equivalent transformation of the problem was carried out. By solving open-loop saddle point strategy off-line in advance, the initial states of the problem and the corresponding solutions were taken as samples for neural network training, and the trained network structure can quickly obtain the approximate solution of the corresponding problem. In order to better deal with the measurement noise in the game environment, a receding horizon optimization framework was designed based on the neural network structure. By periodically solving the problem, the rendezvous of the pursuer and evader was finally realized. Numerical simulation shows that the proposed strategy can effectively deal with the uncertainty of measurement noise, and compared with the existing strategy in the literature, the calculation time can be reduced from minutes to several seconds. |
| Keywords: pursuit-evasion game differential games deep neural network receding horizon optimization |
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