A time-coordination reentry guidance law using deep neural networks for Morphing aircrafts is developed in this paper. The neural network fits the mapping from states, guidance and morphing parameters to flight performances using the dataset generated by traversing bank angle profile and morphing parameters. In the guidance law, leveraging the automatic differentiation property of the neural network and Newton iteration methods, guidance and morphing parameters matching the expected range and flight time are determined. Lateral guidance is conducted based on the exponential convergence criterion for bank angle flips. Simulation results demonstrate that multiple morphing aircrafts satisfy path constraints and achieve the desired guidance accuracy, providing sufficient evidence for the effectiveness of the time-coordination guidance law.


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    Title :

    Time-Coordination Entry Guidance for Unpowered Gliding Morphing Aircrafts Using Deep Neural Networks


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Xu, Ziqi (author) / Zhu, Jialin (author) / Gong, Shengping (author) / Li, Tianren (author)

    Conference:

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



    Publication date :

    2025-03-05


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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