导弹突防后弹道机动调整策略强化学习
Reinforcement learning of ballistic maneuver adjustment strategy after missile penetration
樊博璇 1陈桂明 2韩磊 3李冰3
作者信息
- 1. 火箭军工程大学作战保障学院,陕西西安 710025;火箭军装备部驻西安地区第一军事代表室,陕西西安 710025
- 2. 火箭军工程大学作战保障学院,陕西西安 710025
- 3. 火箭军装备部驻西安地区第一军事代表室,陕西西安 710025
- 折叠
摘要
针对弹道导弹中段突防后飞行弹道与标准弹道产生较大偏离的弹道机动调整问题,建立了机动调整时机策略最优化模型.设计了机动调整逆序Q学习算法,采用Tile coding逼近器编码状态特征空间,并对其进行线性逼近.构建了Q学习算法与蒙特卡罗方法相结合的逆序更新策略机制,以对导弹机动调整最优时机进行训练.仿真测试分析结果表明,在给定场景参数下,通过10 000 代强化学习算法训练得到的策略能够可靠地使用最少机动次数控制导弹突防后飞行弹道的调整决策,验证了方法的有效性.
Abstract
In order to solve the problem of trajectory maneuver adjustment caused by large deviation of flight trajectory after midcourse penetration of ballistic missile,an optimization model of maneuver adjustment timing strategy was established.A reverse sequence Q learning algorithm for maneuver adjustment was designed,and a Tile coding approximator encoding was used to encode the state characteristics space,and the space was linearly approximated.A reverse-order update strategy mechanism combining Q learning algorithm and Monte Carlo method was constructed,the optimal timing of missile maneuvering adjustment was trained.The simulation results show that the strategy obtained by training 10 000 generations of reinforcement learning algorithm can reliably control the adjustment decision of flight trajectory after missile penetration with the minimum maneuver times under given scenario parameters,which verifies the effectiveness of the method.
关键词
弹道导弹/中段突防/强化学习/Q学习/控制决策Key words
ballistic missile/midcourse penetration/reinforcement learning/Q learning/control decision引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据