基于自学习微分对策的主动防御制导方法

Active Defense Guidance Based on Self-Learning Differential Game

主要对三体对抗场景下的主动防御制导方法进行研究。首先,通过构造具有严格反馈形式的三体对抗模型,结合Backstepping理论和微分对策思想推导出一种主动防御制导策略;其次,基于自适应动态规划算法建立评价神经网络以自学习在线求解该制导方法,并利用Lyapunov稳定性理论证明了闭环系统的稳定性和评价网络权值的收敛性;最终,仿真验证了所设计的主动防御制导方法的有效性。

This paper mainly studies the active defense guidance method in three-body confrontation scenario. Firstly,an active defense guidance method is derived by constructing a three-body confrontation model with strict feedback form,combined with Backstepping theory and differential game idea.Secondly,an evaluation neural network is established based on the Adaptive Dynamic Programming(ADP) algorithm to solve the guidance method online,and Lyapunov stability theory is used to prove the stability of the closed-loop system and the convergence of the evaluation network weights.Finally,simulation results verify the effectiveness of the proposed active defense guidance method.