High-dynamic intelligent maneuvering guidance strategy via deep reinforcement learning

Abstract

Aiming at the coordination between maneuvering penetration and high-precision guidance under complex flight missions of high-velocity vehicle, the manuscript studies a three-dimensional high-dynamic intelligent maneuvering guidance strategy based on optimal control and deep reinforcement learning (DRL). A three-dimensional attack–defense model is established, and maneuver guidance mission is decomposed into longitudinal and lateral directions. In the longitudinal direction, maneuvering model with the instantaneous miss distance as the control variable is constructed, and the maximum value principle is employed to obtain the optimal maneuver duration and start timing. In the lateral direction, Markov decision process model of maneuver guidance is proposed by synthesizing the guidance error and miss distance of encounter point, and the reward function is designed by considering maneuver and guidance performance. The DRL method is used to learn and train the maneuver strategy, and the training process is improved as well. The simulation results show that the intelligent maneuvering guidance strategy can improve the penetration performance, reduce influence of maneuver flight on the guidance accuracy, and ensure the adaptability under changeable flight missions.