While machine learning and spacecraft autonomy continue to gain research interest, significant work remains to be done in efficiently applying modern machine learning techniques to problems in space ight. This study presents a framework for deriving a discrete neural spacecraft attitude controller using reinforcement learning, a paradigm of machine learning, without the need for high-performance computing. The developed attitude controller is an approximately time-optimal solution to a highly constrained control problem, able to achieve well above industry-standard pointing accuracies. Control examples are also presented of the agent performing large-angle spacecraft slews in the developed simulation environment and future extensions of this work are discussed.


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    Titel :

    Autonomous Spacecraft Attitude Control Using Deep Reinforcement Learning


    Beteiligte:
    J. G. Elkins (Autor:in) / R. Sood (Autor:in) / C. Rumpf (Autor:in)

    Erscheinungsdatum :

    2020


    Format / Umfang :

    13 pages


    Medientyp :

    Report


    Format :

    Keine Angabe


    Sprache :

    Englisch


    Schlagwörter :


    AUTONOMOUS SPACECRAFT ATTITUDE CONTROL USING DEEP REINFORCEMENT LEARNING

    Elkins, Jacob / Sood, Rohan / Rumpf, Clemens | TIBKAT | 2021


    Autonomous Spacecraft Attitude Control Using Deep Reinforcement Learning

    Jacob G. Elkins / Rohan Sood / Clemens Rumpf | NTRS


    REINFORCEMENT LEARNING FOR SPACECRAFT ATTITUDE CONTROL

    Vedant, Fnu / Allison, James / West, Matthew et al. | TIBKAT | 2020


    Attitude Control of Spacecraft Based on Deep Reinforcement Learning TD3 Algorithm

    Peng, Zhuoyue / Shen, Qiang / Song, Chi | Springer Verlag | 2025