Real-Time Reentry Trajectory Planning of Hypersonic Vehicles: A Two-Step Strategy Incorporating Fuzzy Multiobjective Transcription and Deep Neural Network

Abstract

A two-step strategy is developed for real-time trajectory planning of a hypersonic vehicle (HV) in the reentry phase. The first step generates the optimal trajectory for the HV using a recently proposed fuzzy multiobjective transcription method. In the second step, the optimally generated trajectories are utilized to train a deep neural network (DNN), which is then acted as the optimal command generator in real time. A detailed simulation study is carried out to verify the effectiveness and real-time applicability of the proposed integrated design. The DNN-driven controller is further compared against other optimization-based techniques existing in relative works. Moreover, extension works on the real-time trajectory planning of a six-degree-of-freedom HV model are performed. The results confirm the feasibility and reliability of applying the proposed method for the planning of the HV entry flight path in real time.