A generic backtracking process algorithm based on the deductive implementation of a probabilistic Markov/cell-to-cell mapping technique is proposed for risk-informed identification of critical scenarios involving control systems of unmanned aerial systems operating in the National Airspace. A discrete state representation of the unmanned aerial system control system is used that allows tracing fault propagation throughout the system and the quantification of probabilistic system evolution in time. A hybrid state unmanned aerial system control system composed of a discrete state flight executive based on a finite state machine representation of the control system and a continuous state autopilot based on linear control methods is used as a case study. The consequence of interest in the case study is taken to be a deviation from the flight plan at the destination in the waypoint following phase of an unmanned aerial system mission under a “link condition” system configuration. The backtracking process algorithm is implemented to identify the risk-significant scenarios for the case study.


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    Titel :

    Quantitative and Risk-Based Framework for Unmanned Aircraft Control System Assurance


    Beteiligte:
    Hejase, Mohammad (Autor:in) / Kurt, Arda (Autor:in) / Aldemir, Tunc (Autor:in) / Özgüner, Ümit (Autor:in) / Guarro, Sergio B. (Autor:in) / Yau, Michael K. (Autor:in) / Knudson, Matt D. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.02.2018




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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