AbstractThe traditional method used for handling qualities assessment of manned space vehicles is too time-consuming to meet the requirements of an increasingly fast design process. In this study, a rendezvous and docking inverse simulation system to assess the handling qualities of spacecraft is proposed using a previously developed model-predictive-control architecture. By considering the fixed discrete force of the thrusters of the system, the inverse model is constructed using the least squares estimation method with a hyper-ellipsoidal restriction, the continuous control outputs of which are subsequently dispersed by pulse width modulation with sensitivity factors introduced. The inputs in every step are deemed constant parameters, and the method could be considered as a general method for solving nominal, redundant, and insufficient inverse problems. The rendezvous and docking inverse simulation is applied to a nine-degrees-of-freedom platform, and a novel handling qualities evaluation scheme is established according to the operation precision and astronauts' workload. Finally, different nominal trajectories are scored by the inverse simulation and an established evaluation scheme. The scores can offer theoretical guidance for astronaut training and more complex operation missions.

    HighlightsAn inverse simulation system for manually controlled RVD is established.The assessments scheme for handling qualities is proposed.The RVD operation of the astronaut is evaluated by the scheme proposed in this paper.


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    Titel :

    Inverse simulation system for evaluating handling qualities during rendezvous and docking


    Beteiligte:
    Zhou, Wanmeng (Autor:in) / Wang, Hua (Autor:in) / Thomson, Douglas (Autor:in) / Tang, Guojin (Autor:in) / Zhang, Fan (Autor:in)

    Erschienen in:

    Acta Astronautica ; 137 ; 461-471


    Erscheinungsdatum :

    2017-05-13


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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