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A Novel Sensor Fault Detection in an Unmanned Quadrotor Based on Adaptive Neural Observer

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Abstract

Prompt detection and isolation of faults and failures in flight control systems are crucial to avoid negative impacts on human and environmental systems, and to the system itself. In this study, a new scheme based on a nonlinear dynamic model is designed for sensor fault detection and isolation in an unmanned aerial vehicle (UAV) system. In the proposed design, a neural network is used as an observer for faults in the UAV sensors. The weighting parameters of the neural network are updated by the Extended Kalman Filter (EKF). The designed fault detection (FD) system is applied to an unmanned quadrotor model, and the simulation results show that the proposed design is capable of the prompt detection of sensor faults.

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Authors and Affiliations

  1. Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Semnan, Iran

    Payam Aboutalebi

  2. Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA

    Alireza Abbaspour & Parisa Forouzannezhad

  3. Department of Electrical Engineering, Florida Polytechnic University, Lakeland, FL, USA

    Arman Sargolzaei

Authors
  1. Payam Aboutalebi
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  2. Alireza Abbaspour
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  3. Parisa Forouzannezhad
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  4. Arman Sargolzaei
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Correspondence to Alireza Abbaspour.

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Aboutalebi, P., Abbaspour, A., Forouzannezhad, P. et al. A Novel Sensor Fault Detection in an Unmanned Quadrotor Based on Adaptive Neural Observer. J Intell Robot Syst 90, 473–484 (2018). https://doi.org/10.1007/s10846-017-0690-7

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  • DOI: https://doi.org/10.1007/s10846-017-0690-7

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