This paper presents autotuned primal–dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically feasible trajectory solutions for constrained hypersonic reentry optimal control problems across a large mission parameter space. In Auto-SCvx, the authors solve a sequence of convex subproblems until convergence to a solution of the original nonconvex problem. This method iteratively optimizes dual variables in closed form in order to update the penalty hyperparameters used in the primal variable updates. A benefit of this method is that it is autotuning and requires no hand tuning by the user with respect to the constraint penalty weights. Several example hypersonic reentry problems are posed and solved using this method, and comparative studies are conducted against current methods. In these numerical studies, our algorithm demonstrates equal and often improved performance while not requiring hand tuning of penalty hyperparameters.


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    Titel :

    Autotuned Primal–Dual Successive Convexification for Reentry Guidance


    Beteiligte:
    Mceowen, Skye (Autor:in) / Calderone, Daniel J. (Autor:in) / Tiwary, Aman (Autor:in) / Zhou, Jason S. K. (Autor:in) / Kim, Taewan (Autor:in) / Elango, Purnanand (Autor:in) / Açıkmeşe, Behçet (Autor:in)


    Erscheinungsdatum :

    01.07.2025




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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