Research on Optimization Design Method of Autonomous Deformation Decision for Intelligent Morphing Aircraft

Intelligent morphing aircraft can timely and independently change its shape according to the flight mission and environment, and meet different flight missions with different aerodynamic layouts, so as to achieve performance improvement and trajectory optimization in different flight stages. So it is also one of the development trends that is most likely to bring about the technical revolution of the future aerospace aircraft. In order to improve the trajectory characteristics, it is necessary to establish the controlled model in advance by using traditional controller to control structural changes. However, due to obvious changes in the structure of morphing aircraft, it is impossible to establish accurate mathematical model. Therefore, an intelligent trajectory optimization method is proposed to solve the problem of aircraft autonomous deformation decision control. This paper takes intelligent morphing aircraft flying at high speed in large airspace as the research object, aiming at the technical problems that the aircraft is difficult to obtain sufficient deformable flight test data in advance, which leads to the difficulty in predicting the optimal aerodynamic shape under different flight states, and the traditional controller cannot be used to optimize the deformation. A deformable decision scheme based on reinforcement learning (RL) network is proposed, which realizes that the aircraft structure can be changed independently according to the real-time state in flight, so as to improve the aerodynamic performance and optimize the flight trajectory.