Predictor–Corrector Guidance for a Hypersonic Morphing Vehicle

In an effort to address the problem of hypersonic morphing vehicles reaching the target while avoiding no-fly zones, an improved predictor–corrector guidance method is proposed. Firstly, the aircraft motion model and the constraint model are established. Then, the basic algorithm is given. The Q-learning method is used to design the attack angle and sweep angle scheme to ensure that the aircraft can fly over low-altitude zones. The B-spline curve is used to determine the locations of flight path points, and the bank angle scheme is designed using the predictor–corrector method, so that the aircraft can avoid high-altitude zones. Next, the Monte Carlo reinforcement learning (MCRL) method is used to improve the predictor–corrector method and a Deep Neural Network (DNN) is used to fit the reward function. The planning method in this paper realizes the use of a variable sweep angle, while the improved method further improves the performance of the trajectory, including the attainment of greater final speed and a smaller turning angle. The simulation results verify the effectiveness of the proposed algorithm.