基于强化学习的最优控制指令模仿生成方法  

Imitative Generation of Optimal Guidance Law Based on Reinforcement Learning

作　　者：贾政轩 林廷宇 肖莹莹 施国强 王豪[2] 曾贲 欧一鸣 赵芃芃 Jia Zhengxuan;Lin Tingyu;Xiao Yingying;Shi Guoqiang;Wang Hao;Zeng Bi;Ou Yiming;Zhao Pengpeng(Beijing Simulation Center,Beijing 100854,China;Beijing Institute of Electronic System Engineering,Beijing 100854,China)

机构地区：[1]北京仿真中心,北京100854 [2]北京电子工程总体研究所,北京100854

出　　处：《系统仿真学报》2023年第11期2410-2418,共9页Journal of System Simulation

摘　　要：以高速机动目标拦截为问题背景,基于深度强化学习提出了一种不依赖目标加速度估计的逆轨拦截最优控制指令生成方法,并通过仿真实验进行了有效性验证。从仿真实验结果看,提出的方法实现了三维空间高速机动目标逆轨拦截并大幅削减了对带有强不确定性目标估计的要求,相比最优控制方法具有更强的适用性。Under the background of high-speed maneuvering target interception,an optimal guidance law generation method for head-on interception independent of target acceleration estimation is proposed based on deep reinforcement learning.In addition,its effectiveness is verified through simulation experiments.As the simulation results suggest,the proposed method successfully achieves head-on interception of high-speed maneuvering targets in 3D space and largely reduces the requirement for target estimation with strong uncertainty,and it is more applicable than the optimal control method.

关 键 词：强化学习 最优制导 模仿学习 逆轨拦截 制导控制 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]