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兵工学报 ›› 2021, Vol. 42 ›› Issue (7): 1400-1411.doi: 10.3969/j.issn.1000-1093.2021.07.007

• 论文 • 上一篇    下一篇

基于纵程解析解的飞行器智能横程机动再入协同制导

张晚晴1, 余文斌1, 李静琳2, 陈万春1   

  1. (1.北京航空航天大学 宇航学院, 北京 100191; 2.北京宇航系统工程研究所, 北京 100076)
  • 上线日期:2021-07-30
  • 作者简介:张晚晴(1995—),女,博士研究生。E-mail: zhangwanqing@buaa.edu.cn
  • 基金资助:
    国家自然科学基金项目(62003012)

Cooperative Reentry Guidance for Intelligent Lateral Maneuver of Hypersonic Vehicle Based on Downrange Analytical Solution

ZHANG Wanqing1, YU Wenbin1, LI Jinglin2, CHEN Wanchun1   

  1. (1.School of Astronautics, Beihang University, Beijing 100191, China; 2.Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China)
  • Online:2021-07-30

摘要: 针对高超声速飞行器协同饱和打击需求,提出一种基于深度Q-学习网络(DQN)算法的深度强化学习横程机动再入协同制导方法。解耦设计高超声速飞行器横纵制导方法,基于高精度的纵程解析解,解析计算纵向升阻比得到倾侧角模值。抽象横向制导倾侧反转逻辑为马尔可夫决策问题,引入强化学习思想,设计一种基于DQN算法的横向智能机动决策器,构建智能体离线学习-在 线调用模式,计算倾侧角剖面的符号变化。以典型高超声速飞行器CAV-H为对象,基于数学分析MATLAB平台通过弹道仿真对该制导方法进行验证。仿真结果表明:新制导方法制导精度高,任务适应性强,可以在线使用,能够严格满足飞行时间约束和能量管理需求;相比于基于三维解析解的再入协同制导方法,新制导方法可以更大程度发挥飞行器的横向机动能力,具备更高的突防潜力。

关键词: 高超声速飞行器, 再入协同制导, 纵程解析解, 深度强化学习, 深度Q-学习网络

Abstract: A cooperative guidance law based on deep Q-learning network (DQN) algorithm for lateral maneuver of hypersonic vehicles is proposed to meet the requirement of cooperative saturation attack. The longitudinal and lateral guidance laws are deigned. The longitudinal lift-to-drag ratio is calculated to obtain the modulus of bank angle based on the high-precision longitudinal analytical solution. The lateral bank reversal logic is abstracted as a Markov decision process (MDP), and the reinforcement learning method can be used. A lateral intelligent maneuver decision-making device based on DQN algorithm is designed. The intelligent agent can be generated by offline training according to the mission requirements, and is called online to generate the sign of bank angle. Simulated results show that the proposed guidance law can autonomously generate bank angle reversals on-line, strictly meets the requirements of flight time constraints and energy management, and has high guidance accuracy and good mission adaptability. Compared with the coordinative reentry guidance law based on three-dimensional analytical solutions, the proposed guidance law is used to fully achieve the lateral maneuverability of vehicle and make it have higher penetration potential.

Key words: hypersonicvehicle, cooperativereentryguidance, downrangeanalyticalsolution, deepreinforcementlearning, deepQ-learningnetwork

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