基于纵程解析解的飞行器智能横程机动再入协同制导

针对高超声速飞行器协同饱和打击需求,提出一种基于深度Q-学习网络(DQN)算法的深度强化学习横程机动再入协同制导方法.解耦设计高超声速飞行器横纵制导方法,基于高精度的纵程解析解,解析计算纵向升阻比得到倾侧角模值.抽象横向制导倾侧反转逻辑为马尔可夫决策问题,引入强化学习思想,设计一种基于DQN算法的横向智能机动决策器,构...

Cooperative Reentry Guidance for Intelligent Lateral Maneuver of Hypersonic Vehicle Based on Downrange Analytical Solution

A cooperative guidance law based on deep Q-learning network (DQN) algorithm for lateral maneuver of hypersonic vehicles is proposed to meet the requirement of cooperative saturation attack. The longitudinal and lateral guidance laws are deigned. The longitudinal lift-to-drag ratio is calculated to obtain the modulus of bank angle based on the high-precision longitudinal analytical solution. The lateral bank reversal logic is abstracted as a Markov decision process (MDP), and the reinforcement learning method can be used. A lateral intelligent maneuver decision-making device based on DQN algorithm is designed. The intelligent agent can be generated by offline training according to the mission requirements, and is called online to generate the sign of bank angle. Simulated results show that the proposed guidance law can autonomously generate bank angle reversals on-line, strictly meets the requirements of flight time constraints and energy management, and has high guidance accuracy and good mission adaptability. Compared with the coordinative reentry guidance law based on three-dimensional analytical solutions, the proposed guidance law is used to fully achieve the lateral maneuverability of vehicle and make it have higher penetration potential.