A learning-based predictive-corrector guidance method for hypersonic vehicles with a high lift-to-drag ratio is proposed in this paper. First, based on the quasi equilibrium-glide condition, a traditional predictive-corrector guidance algorithm is employed to address reentry guidance with path constraint. Then, to avoid the ubiquitous phenomenon of large phugoid oscillation, a learning-based altitude rate feedback mechanism is proposed. The feedback gain is scheduled adaptively by a deep reinforcement learning strategy to enhance the adaptability and robustness of the guidance algorithm in different flight environments. Finally, the numerical simulation demonstrates the effectiveness of the proposed algorithm.


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    Titel :

    Learning-Based Predictive-Corrector Reentry Guidance for Hypersonic Vehicles


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Wu, Tiancai (Autor:in) / Wang, Honglun (Autor:in) / Liu, Yiheng (Autor:in) / Yu, Yue (Autor:in) / Lun, Yuebin (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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