Abstract: Intelligent morphing aircraft is a new generation of aircraft that can timely and autonomously change the structural shape according to the changes of flight mission and environment,and meet the requirements of different flight stages with diffe-rent aerodynamic layouts.It is considered as one of the development trends that is most likely to bring about the technological change of aerospace vehicles in the future.However,large structural deformation makes it difficult to establish an accurate mathematical model.Therefore,it is proposed to use the model-free reinforcement learning(RL) algorithm to realize the autonomous decision-making of trajectory optimization through interactive learning.This paper takes intelligent morphing aircraft flying at high speed in large airspace as the research object,aiming at the technical problems that it is difficult to obtain sufficient defor-mable flight test data in advance,which makes it difficult to predict the optimal aerodynamic shape under different flight states,a deformable decision optimization design scheme based on RL network model is proposed.The deformations can be made independently according to real-time conditions during flight,so as to achieve the mission objectives of improving aerodynamic performance and optimizing flight trajectory.

Key words: Morphing aircraft, Reinforcement learning, Intelligent decision-making, optimal aerodynamic shape optimization, trajectory optimization