This paper is concerned with the design and study of an adaptive formation—maintaining control of two satellites in low-Earth orbits. The purpose of the control is to maintain a projective circular orbit. It is assumed that one of the spacecraft is not controllable and its ballistic coefficient is unknown. The other spacecraft is controllable, and it is possible to control both its effective cross section area and orbital motion by using a high-thrust engine. Artificial neural networks are used to approximate the control functions and adapt to the uncertain ballistic coefficient of the uncontrolled spacecraft. Implementation, architecture, learning processes, and quality evaluation scheme of the neural networks are described in detail. The procedure for real-time adaptation to an unknown parameter of an uncontrolled vehicle is given. It is shown that the real-time adaptation is possible within an acceptable time interval. The results of the adaptation to two parameters—the ballistic coefficient and the atmospheric density—are also presented. The simulation is performed within the context of the perturbed two-body problem that takes into account the atmospheric drag and the second zonal harmonic effect.


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    Titel :

    Adaptive Neural Formation-Keeping Control for Satellites in a Low-Earth Orbit


    Weitere Titelangaben:

    Cosmic Res


    Beteiligte:

    Erschienen in:

    Cosmic Research ; 59 , 6 ; 501-516


    Erscheinungsdatum :

    2021-11-01


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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