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

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.

AI法律、法律AI及“第三道路”

近年来,我国关于法律与人工智能的研究成果颇丰,并呈现出两种研究进路。AI法律进路旨在把AI纳入法律规制框架之内,而法律AI进路则力图把法律转化为智能系统,这就决定了二者不同的方向与立场、领域与路径,也带来了不同的问题与前景。深入分析表明,我们需要从浪漫理想回归现实主义,开辟汇合二者、扬长避短的"第三道路",增强法学与技术的对话合作,促进法律与人工智能的双向融合,从而有效推进法律与人工智能领域的深入研究。

智能化最优滑模制导律研究

针对机动目标的空间拦截问题,文中介绍了一种对参数摄动和外干扰具有鲁棒性的最优滑模制导律,并对其智能化实现方法进行了研究。仿真结果表明,这种智能化的最优滑模制导律对于机动目标具有较好的拦截效果。

基于DDPG算法的导弹末制导律设计

传统比例导引律缺少时间和视场角的限制,在面对现代空战目标机动快的情况下,拦截效果也不理想。针对该问题,提出了基于深度强化学习的智能导弹控制律设计。该算法以导弹当前态势为状态输入,考虑到导弹追击过程中时间和视场角的限制,以此设计奖励函数,在连续的状态空间和动作空间中,导弹能根据当前态势自主选择正确的制导指令,及时追上机动目标。实验表明,该算法相比传统比例导引律,具有更强的鲁棒性,同时,导弹也具有了自主决策的能力。