Sequential second-order cone programming (SSOCP) is commonly used in aerospace applications for solving nonlinear trajectory optimization problems. The SSOCP possesses good real-time performance. However, one long-standing challenge is its unguaranteed convergence. In this paper, we theoretically analyze the descent property of the L 1 penalty function in the SSOCP. Using Karush–Kuhn–Tucker conditions, we obtain two important theoretical results: 1) the L 1 penalty function of the original nonlinear problem always descends along the iteration direction; 2) a sufficiently small trust region can decrease the L 1 penalty function. Based on these two results, we design an improved trust region shrinking algorithm with theoretically guaranteed convergence. In numerical simulations, we verify the proposed algorithm using a reentry trajectory optimization problem.


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    Titel :

    Descent Property in Sequential Second-Order Cone Programming for Nonlinear Trajectory Optimization


    Beteiligte:
    Xie, Lei (Autor:in) / Zhou, Xiang (Autor:in) / Zhang, Hong-Bo (Autor:in) / Tang, Guo-Jian (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-10-18


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch