航天器轨道追逃动力学与控制问题研究综述  

Survey on dynamics and control problem research in spacecraft orbital pursuit-evasion game

作　　者：朱彦伟[1] 张乘铭 杨傅云翔 杨乐平[1] ZHU Yanwei;ZHANG Chengming;YANG Fuyunxiang;YANG Leping(College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学空天科学学院,湖南长沙410073

出　　处：《国防科技大学学报》2024年第3期1-11,共11页Journal of National University of Defense Technology

基　　金：国防科技大学自主创新科学基金资助项目(22-ZZCX-083)。

摘　　要：随着航天器交会与接近操作技术的快速发展,轨道追逃问题逐渐成为航天领域的研究热点。从动力学与控制视角,对航天器轨道追逃问题的研究现状进行综述。给出了基于定量微分对策的轨道追逃问题模型的一般形式,系统梳理了各种类型的轨道追逃问题;对于追逃策略求解,分别针对闭环策略和开环策略,分析了各种方法的优缺点;围绕人工智能算法与轨道追逃问题的结合,阐述了基于深度神经网络和强化学习的轨道追逃策略的研究现状。关于未来展望,提出了追逃博弈态势分析、多航天器博弈控制、三体条件下博弈动力学与控制等发展方向。With the rapid development of spacecraft rendezvous and proximity operation technology,the problem of orbital pursuit-evasion has gradually become a research hotspot in the aerospace field.From the perspective of dynamics and control,the research status of spacecraft orbital pursuit-evasion was reviewed.General form of the orbital pursuit-evasion problem model based on quantitative differential games was given,and various types of orbital pursuit-evasion problems were systematically sorted out.For the solution of pursuit and escape strategies,the advantages and disadvantages of various methods were analyzed for closed-loop strategy and open-loop strategy.Focusing on the combination of artificial intelligence algorithm and orbital pursuit and escape problem,the research status of orbital pursuit and escape strategy based on deep neural network and reinforcement learning was expounded.Regarding future prospects,development directions has been proposed,including the pursuit-evasion game situation analysis,the multi-spacecraft game control,the game dynamics and control under three-body problem.

关 键 词：航天器追逃博弈 微分对策 深度神经网络 强化学习 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]