Atmospheric powered descent guidance (APDG) can be solved by successive convexification; however, its onboard application is impeded by high computational cost. When aerodynamic forces are ignored, powered descent guidance (PDG) can be converted to a single convex problem. In contrast, APDG has to be converted into a sequence of convex subproblems, each of which is significantly more complicated. Consequently, the computation increases sharply. A fast real-time interior point method was presented to solve the correlated convex subproblems efficiently onboard in the work. The main contributions are as follows: First, an algorithm was proposed to accelerate the solution of linear systems that cost most of the computation in each iterative step by exploiting the specific problem structure. Second, a warm-starting scheme was introduced to refine the initial value of a subproblem with a rough approximate solution of the former subproblem, which lessened the iterative steps required for each subproblem. The method proposed reduced the run time by a factor of 9 compared with the fastest publicly available solver tested in Monte Carlo simulations to evaluate the efficiency of solvers. Runtimes on the order of 0.6 s are achieved on a radiation-hardened flight processor, which demonstrated the potential of the real-time onboard application.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance


    Beteiligte:
    Chen, Yushu (Autor:in) / Yang, Guangwen (Autor:in) / Wang, Lu (Autor:in) / Chen, Haipeng (Autor:in) / Gan, Qingzhong (Autor:in) / Xu, Quanyong (Autor:in)


    Erscheinungsdatum :

    2023-10-01


    Format / Umfang :

    1524409 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Learning-Based Onboard Guidance for Fuel-Optimal Powered Descent

    You, Sixiong / Wan, Changhuang / Dai, Ran et al. | AIAA | 2021


    Implementation and Experimental Demonstration of Onboard Powered-Descent Guidance

    Scharf, Daniel P. / Açıkmeşe, Behçet / Dueri, Daniel et al. | AIAA | 2016


    ONBOARD GUIDANCE FOR REUSABLE ROCKETS: AERODYNAMIC DESCENT AND POWERED LANDING

    Sagliano, Marco / Heidecker, Ansgar / Hernández, José Macés et al. | TIBKAT | 2021