title: Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.
abstract: A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.
authors.affiliation: Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA. Electronic address: alireza.abaspour@gmail.com.
authors.forename: Alireza
authors.initials: A
authors.lastname: Abbaspour
authors.affiliation: (c)Department of Electrical Engineering, Shahrood University of Technology, Shahrood, Semnan, Iran.
authors.forename: Payam
authors.initials: P
authors.lastname: Aboutalebi
authors.affiliation: Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
authors.forename: Kang K
authors.initials: KK
authors.lastname: Yen
authors.affiliation: (b)Department of Electrical Engineering, Florida Polytechnic University, Lakeland, FL, USA.
authors.forename: Arman
authors.initials: A
authors.lastname: Sargolzaei
keywords: Adaptive fault detection; Nonlinear dynamic model; Sensor and actuator faults; Unmanned aerial vehicle