Abstract: The breakthrough of artificial intelligence provides a new technical approach for the research on aircraft reentry guidance. In both of the reference trajectory tracking guidance and predictor-corrector guidance, flight corridor parameters need to be designed based on manual experience in advance. In this paper, we propose to break through the constraint of "conical" flight path envelope, which is common in traditional guidance methods, by taking the natural advantage of reinforcement learning in intelligent decision making. Under the premise of satisfying dynamic equations and hard conditions such as heating rate, load factor and dynamic pressure, a large number of "trial-and-error" interactions can be taken between the aircraft and environment. Effective reward is set by referring to the human's idea of adjusting learning strategies based on feedback. Proximal Policy Optimization (PPO) algorithm in reinforcement learning is employed to train the bank angle guidance model, so as to generate bank angle instruction online based on real-time state information. The "new quality" flight corridor is explored, which is completely different from the traditional guidance method. Monte Carlo simulation analysis verifies that the intelligent guidance technology based on reinforcement learning can fully exploit the advantage of wide range flight of aircraft and further expand the flight profile of aircraft.

Key words: intelligent guidance, new quality flight corridor, reinforcement learning, PPO algorithm, artifical intelligence