Computational intelligence interception guidance law using online off-policy integral reinforcement learning  

作　　者：WANG Qi LIAO Zhizhong 

机构地区：[1]China Airborne Missile Academy,Luoyang 471009,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第4期1042-1052,共11页系统工程与电子技术（英文版）

摘　　要：Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

关 键 词：two-person zero-sum differential games Hamilton–Jacobi–Isaacs(HJI)equation off-policy integral reinforcement learning(IRL) online learning computational intelligence inter-ception guidance(CIIG)law 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TJ765[自动化与计算机技术—控制科学与工程]