Hypersonic aircraft reentry cooperative guidance method based on reinforcement learning

Abstract

The invention discloses a hypersonic flight vehicle reentry cooperative guidance method based on reinforcement learning, and particularly relates to a hypersonic flight vehicle reentry cooperative guidance method based on reinforcement learning. Establishing a hypersonic velocity reentry kinetic model and a multi-constraint reentry model; designing an attack angle profile and a height energy profile, and obtaining analytic solutions of the attack angle and the heeling angle; intelligent decision making is carried out on heeling angle symbols according to a DQN algorithm, the action space of the heeling angle symbols is expanded, and a step-shaped mixed reward function is designed by considering time collaboration and falling angle collaboration; the hypersonic aircraft reentry cooperative guidance method based on intelligent reinforcement learning is obtained by training a heeling angle intelligent decision-making model offline and giving cooperative time and a cooperative fall angle to obtain a guidance instruction online, the defect that in an aircraft guidance strategy, heeling angle symbols are turned frequently is effectively overcome, time cooperation and fall angle cooperation are met, and the guidance efficiency is improved. A simulation experiment verifies that the method can well consider time and fall angle cooperation to carry out multi-hypersonic-velocity aircraft guidance.

Description

technical field [0001] The invention relates to the technical field of hypersonic aircraft re-entry cooperative guidance, in particular to a hypersonic aircraft re-entry cooperative guidance method based on reinforcement learning. Background technique [0002] Re-entry coordinated guidance of hypersonic vehicles is one of the core and focus of hypersonic vehicle research in recent years. It is still an unsolved problem for many countries to use multiple hypersonic vehicles to achieve time coordination and landing angle coordination at the same time. [0003] Hypersonic vehicles are inherently characterized by strong coupling, strong nonlinearity, and strong uncertainty. The trajectory optimization and guidance of a single aircraft is very difficult. It is conceivable that the complexity of the trajectory optimization and guidance problems of multiple hypersonic vehicles It is bound to increase sharply. Coupled with the constraints of time coordination, the research on the ti...

AI Technical Summary

Problems solved by technology

The nominal trajectory guidance method relies on the pre-planned reentry trajectory, which is difficult to meet the autonomy requirements of the lift-type reentry vehicle in the future

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