Real-time estimation of dynamic model parameters for an unmanned aircraft can provide valuable information about the dynamics of the vehicle. However, the aircraft model estimation performance can be severely degraded with an active control system and highly collinear model terms such as those found on a quadrotor unmanned aircraft. Model parameters were estimated based on dynamic flight data of a quadcopter aircraft. System identification maneuvers were generated using varying-amplitude orthogonal multisine input signals applied on a quadcopter with nominal hardware conditions and with a damaged propeller. Recursive Fourier transform regression was applied to dynamic data to estimate parameters of two input model forms: independent and delta input. Prediction capability of the identified models was assessed using data of nearly the same flight conditions. Results indicated that, as the amplitude of the input multisine increased, the identifiability of control effectiveness improved, and prediction error decreased. Overall, a well-designed multi-input multisine input design and Fourier transform regression applied together provided efficient and accurate real-time parameter estimation that effectively overcame challenges imposed by a system subjected to closed-loop control with collinear model terms.


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    Titel :

    Real-Time Closed-Loop System Identification of a Quadcopter


    Beteiligte:

    Erschienen in:

    Journal of Aircraft ; 56 , 1 ; 324-335


    Erscheinungsdatum :

    2018-09-27


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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