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.


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    Title :

    Hypersonic Vehicle Morphing Decision Founded on Neural Network


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Hou, Linfei (author) / Liu, Hao (author) / Jin, Huanying (author) / Wang, Rui (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Publication date :

    2025-03-07


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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