Nonlinear Estimation of Sensor Faults With Unknown Dynamics for a Fixed Wing Unmanned Aerial Vehicle

Enzo Iglésis, Nadjim Horri, Karim Dahia, James Brusey, Hélène Piet-Lahanier

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    3 Citations (Scopus)
    65 Downloads (Pure)

    Abstract

    In this paper, the estimation of additive inertial navigation sensor faults with unknown dynamics is considered with application to the longitudinal navigation and control of a fixed wing unmanned aerial vehicle. The faulty measurement is on the pitch angle.
    A jump Markov regularized particle filter is proposed for fault and state estimation of the nonlinear aircraft dynamics, with a Markovian jump strategy to manage the probabilistic transitions between the fault free and faulty modes. The jump strategy uses a small number of sentinel particles to continue testing the alternate hypothesis under both fault free and faulty modes. The proposed filter is shown to outperform the regularized particle filter for this application in terms of fault estimation accuracy and convergence time for scenarios involving both abrupt and incipient faults, without prior knowledge of the fault models. The state estimation is also more accurate and robust to faults using the proposed approach. The root-mean-square error for the altitude is reduced by 77% using the jump Markov regularized particle filter under a pitch sensor fault amplitude of up to 10 degrees. Performance enhancement compared to the regularized particle filter was found to be more pronounced when fault amplitudes increase.
    Original languageEnglish
    Title of host publication2021 International Conference on Unmanned Aircraft Systems (ICUAS)
    PublisherIEEE
    Pages404-412
    Number of pages9
    ISBN (Electronic)978-1-6654-1535-4
    DOIs
    Publication statusPublished - 19 Jul 2021
    Event2021 International Conference on Unmanned Aircraft Systems - Athens, Greece
    Duration: 15 Jun 202118 Jun 2021
    http://www.uasconferences.com/2021_icuas/

    Publication series

    Name2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
    ISSN (Print)2373-6720
    ISSN (Electronic)2575-7296

    Conference

    Conference2021 International Conference on Unmanned Aircraft Systems
    Abbreviated titleICUAS '21
    Country/TerritoryGreece
    CityAthens
    Period15/06/2118/06/21
    Internet address

    Bibliographical note

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    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Aerospace Engineering
    • Control and Optimization

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