Abstract This work aims to propose a new technique that considerably helps enhance time and precision needed to identify “Inertia Parameters (IPs)” of a typical Autonomous Space-Robot (ASR). Operations might include, capturing an unknown Target Space-Object (TSO), “active space-debris removal” or “automated in-orbit assemblies”. In these operations generating precise successive commands are essential to the success of the mission. We show how a generalized, repeatable estimation-process could play an effective role to manage the operation. With the help of the well-known Force-Based approach, a new “modular formulation” has been developed to simultaneously identify IPs of an ASR while it captures a TSO. The idea is to reorganize the equations with associated IPs with a “Modular Set” of matrices instead of a single matrix representing the overall system dynamics. The devised Modular Matrix Set will then facilitate the estimation process. It provides a conjugate linear model in mass and inertia terms. The new formulation is, therefore, well-suited for “simultaneous estimation processes” using recursive algorithms like RLS. Further enhancements would be needed for cases the effect of center of mass location becomes important. Extensive case studies reveal that estimation time is drastically reduced which in-turn paves the way to acquire better results.

    Highlights A mathematical tool to manipulate the dynamics of multi-body systems is presented. A general formulation linear w.r.t all inertia parameters of system is developed. Lagrange formula is used to avoid contact forces or transformation of equations. A fast algorithm is presented to identify all inertia parameters of space system. The identification problem during all phases of a typical space mission is studied.


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    Titel :

    Fast estimation of space-robots inertia parameters: A modular mathematical formulation



    Erschienen in:

    Acta Astronautica ; 127 ; 283-295


    Erscheinungsdatum :

    2016-04-26


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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