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

  • Payam Aboutalebi
  • Alireza Abbaspour
  • Parisa Forouzannezhad
  • Arman Sargolzaei


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.


Malfunction Sensor Flight dynamic Nonlinear model Control Extended Kalman Filter 


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Payam Aboutalebi
    • 1
  • Alireza Abbaspour
    • 2
  • Parisa Forouzannezhad
    • 2
  • Arman Sargolzaei
    • 3
  1. 1.Department of Electrical EngineeringShahrood University of TechnologyShahroodIran
  2. 2.Department of Electrical and Computer EngineeringFlorida International UniversityMiamiUSA
  3. 3.Department of Electrical EngineeringFlorida Polytechnic UniversityLakelandUSA

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