This paper presents an intelligent morphing decision method, utilizing neural networks, designed to address the successive autonomic decision challenge faced by hypersonic morphing flight vehicles (HMFV). Initially, a dynamic model of a HMFV with a successive adjustable sweep angle is developed. Then, considering the external disturbance and the heat flux density constraint of the flight process, by segmenting the reference track and add interference, a trajectory sample set is generated by the Legendre pseudo-spectral method. The neural network is trained to automatically adjust the sweep angle during the gliding phase by analyzing the flight state, making intelligent decisions. Finally, the developed intelligent decision-making algorithm is utilized for trajectory optimization. The simulation results demonstrate a significant enhancement in the distance achieved through intelligent decision morphing compared to program-based morphing. The trajectory can be adjusted to enhance the range based on the intelligent decision-making sweep angle of the current flight state.
Hypersonic Vehicle Morphing Decision Founded on Neural Network
Lect. Notes Electrical Eng.
International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024
07.03.2025
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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