Multi-constrained intelligent gliding guidance via optimal control and DQN

Abstract

In order to improve the adaptability and robustness of gliding guidance under complex environments and multiple constraints, this study proposes an intelligent gliding guidance strategy based on the optimal guidance, predictor-corrector technique, and deep reinforcement learning (DRL). Longitudinal optimal guidance was introduced to satisfy the altitude and velocity inclination constraints, and lateral maneuvering was used to control the terminal velocity magnitude and position. The maneuvering amplitude was calculated by the analytical prediction of the terminal velocity, and the direction was learned and determined by the deep Q-learning network (DQN). In the direction decision model construction, the state and action spaces were designed based on the flight status and maneuvering direction, and a reward function was proposed using the terminal predicted state and terminal constraints. For DQN training, initial data samples were generated based on the heading-error corridor, and the experience replay pool was managed according to the terminal guidance error. The simulation results show that the intelligent gliding guidance strategy can satisfy various terminal constraints with high precision and ensure adaptability and robustness under large deviations.