A New Trajectory Optimization Design Method of Smart Morphing Aircraft

With the gradual maturity of materials science, computer science and artificial intelligence technology, the development of smart morphing aircraft that can make deformation decisions quickly and independently according to target requirements like birds, and meet various flight tasks with the best aerodynamic layout has become a common goal of the current engineering and academic fields. By integrating intelligent materials and intelligent algorithms, morphing aircraft can independently optimize their deformation based on environmental perception, thereby enhancing their overall performance and trajectory optimization throughout various flight phases. Due to the significant changes in their structure, establishing an accurate mathematical model becomes challenging, rendering traditional control methods inadequate for achieving intelligent control over flight trajectories. Consequently, a novel intelligent trajectory optimization design method based on the reinforcement learning (RL) algorithm, is proposed to address this issue. This method allows for innovative trials on supersonic or hypersonic cruise missiles and aircrafts, which can be better adapted to various stages from launch to accelerated climb, to cruise flight and final target attack by changing the aerodynamic configuration. It is believed that the preliminary exploration of this research can provide new inspiration for intelligent decision control and flight performance optimization of future aircraft.