航天器交会型轨道追逃策略的滚动时域优化
2024,46(3):21-29
张乘铭
国防科技大学 空天科学学院, 湖南 长沙 410073，zhangchengmingnudt@nudt.edu.cn
朱彦伟
国防科技大学 空天科学学院, 湖南 长沙 410073
杨乐平
国防科技大学 空天科学学院, 湖南 长沙 410073
杨傅云翔
国防科技大学 空天科学学院, 湖南 长沙 410073
国防科技大学 空天科学学院, 湖南 长沙 410073，zhangchengmingnudt@nudt.edu.cn
朱彦伟
国防科技大学 空天科学学院, 湖南 长沙 410073
杨乐平
国防科技大学 空天科学学院, 湖南 长沙 410073
杨傅云翔
国防科技大学 空天科学学院, 湖南 长沙 410073
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。
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。
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)
国防科技大学自主创新科学基金资助项目(22-ZZCX-083)
Receding horizon optimization for spacecraft pursuit-evasion strategy in rendezvous
ZHANG Chengming
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China,zhangchengmingnudt@nudt.edu.cn
ZHU Yanwei
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
YANG Leping
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
YANG Fuyunxiang
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China,zhangchengmingnudt@nudt.edu.cn
ZHU Yanwei
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
YANG Leping
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
YANG Fuyunxiang
College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
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.
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.
收稿日期：
2023-03-14
2023-03-14
