In this research, reinforcement learning techniques are combined with traditional direct shooting methods to generate optimal proximal spacecraft maneuvers. Open- and closed-loop controllers, parameterized by neural networks, are developed for terminally constrained, fuel-optimal relative motion trajectories using three different thrust models. Neurocontroller performance robustness to parametric uncertainty and bounded initial conditions is assessed. This research demonstrates that neurocontrollers offer a flexible and robust alternative approach to the solution of complex controls problems in the space domain and present a promising path forward to more capable, autonomous spacecraft.


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    Titel :

    Optimal and Robust Neural Network Controllers for Proximal Spacecraft Maneuvers


    Beteiligte:
    B. C. George (Autor:in)

    Erscheinungsdatum :

    2019


    Format / Umfang :

    90 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch




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