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Fault Tolerant Estimation of UAV Dynamics via Robust Adaptive Kalman Filter

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Complex Systems

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 55))

Abstract

A covariance scaling based robust adaptive Kalman filter (RAKF) algorithm is developed for the case of sensor/actuator faults. The proposed RAKF uses variable scale factors for scaling the process and measurement noise covariances and eliminating the effect of the faults on the estimation procedure. At first, the existing covariance estimation based adaptation techniques are reviewed. Then the covariance scaling methods with single and multiple factors are discussed. After choosing the efficient adaptation method an overall concept for the RAKF is proposed. In this concept, the filter initially isolates the fault, either in the sensors or in the actuators, and then it applies the required adaptation process such that the estimation characteristic is not deteriorated. The performance of the proposed filters is investigated via simulations for the UAV state estimation problem. The results of the presented algorithms are compared for different types of sensor/actuator faults and recommendations about their application are given within this scope.

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Authors and Affiliations

  1. Aeronautics and Astronautics Faculty, Istanbul Technical University, 34469, Maslak, Istanbul, Turkey

    Chingiz Hajiyev

  2. Institute of Space and Astronautical Science (ISAS), Japan Aerospace Exploration Agency (JAXA), Yoshinodai, 3-1-1, Sagamihara, Kanagawa, Japan

    Halil Ersin Soken

Authors
  1. Chingiz Hajiyev
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  2. Halil Ersin Soken
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Correspondence to Chingiz Hajiyev .

Editor information

Editors and Affiliations

  1. School of Electrical Engineering an, St. Cyril and St. Methodius Univers, Skopje, Macedonia

    Georgi M. Dimirovski

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Hajiyev, C., Soken, H.E. (2016). Fault Tolerant Estimation of UAV Dynamics via Robust Adaptive Kalman Filter. In: Dimirovski, G. (eds) Complex Systems. Studies in Systems, Decision and Control, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-28860-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-28860-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28858-1

  • Online ISBN: 978-3-319-28860-4

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