This chapter expands our development of an autonomous descent guidance algorithm which is able to deal with both the aerodynamic descent and the powered landing phases of a reusable rocket. The method uses sequential convex optimization applied to a Cartesian representation of the equations of motion, and the transcription is based on the use of hp pseudospectral methods. The major contributions of the formulation are a more systematic exploitation and separation of convex and non-convex contributions to minimize the computation of the latter, the inclusion of highly nonlinear terms represented by aerodynamic accelerations, a complete reformulation of the problem based on the use of Euler angle rates as control means, an improved transcription based on the use of a generalized hp pseudospectral method, and a dedicated formulation of the aerodynamic guidance problem for reusable rockets. The approach is demonstrated for a 40 kN-class reusable rocket. Numerical results confirm that the methodology we propose is very effective and able to satisfy all the constraints acting on the system. It is therefore a valid candidate solution to solve the entire descent phase of reusable rockets in real-time.


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    Titel :

    Autonomous Descent Guidance via Sequential Pseudospectral Convex Programming


    Weitere Titelangaben:

    Springer ser. in Astrophysics & Cosmology


    Beteiligte:
    Song, Zhengyu (Herausgeber:in) / Zhao, Dangjun (Herausgeber:in) / Theil, Stephan (Herausgeber:in) / Sagliano, Marco (Autor:in) / Seelbinder, David (Autor:in) / Theil, Stephan (Autor:in)


    Erscheinungsdatum :

    2023-04-16


    Format / Umfang :

    48 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch