Abstract: To address the integrated guidance and control problem of high-speed gliding vehicles in dive phase under terminal impact angle constraints, model uncertainties, and external disturbances, this paper proposes a line-of-sight angle demonstration-guided deep reinforcement learning-based integrated guidance and control method. First, a coupled model of vehicle-target relative motion and attitude dynamics is established. By incorporating task prior information associated with terminal impact angle constraints, an augmented state representation is constructed by integrating flight states, LOS angle error reference profiles, tracking errors, and integral errors. The terminal integrated guidance and control problem is then formulated as a policy optimization problem under an augmented Markov decision process. Subsequently, a backstepping-based expert controller is designed to generate demonstration actions, and an imitation learning mechanism is introduced to provide expert priors for the deep reinforcement learning policy, thereby improving the exploration efficiency during the early training stage. Finally, simulation results demonstrate that the proposed method can satisfy terminal impact angle constraints while achieving policy generation from augmented state features to control commands, and maintains satisfactory robustness under model uncertainties and external disturbances.

Key words: high-speed gliding flight vehicle, integrated guidance and control, terminal impact angle constraint, deep reinforcement learning, imitation learning