Aileron Locking Fault Detection Based on Extended Kalman Filter for UAV
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This paper presents application of Nonlinear Extended Kalman Filter for aileron actuator locking scenario in Unmanned Aerial Vehicles and estimation of states to make comparison between sensor results and estimation results. At first, nonlinear state space system of UAV is formulated. Then, three faulty scenarios including three faulty aileron actuators locking and one nominal scenario is formed. After that, Extended Kalman Filter is applied to estimate the roll rate state at the same time with measurements. Finally, measurement and filter estimations for the roll rate state outcomes are commented. The system is modelled in MATLAB/Simulink. The performances of the method have been commented using simulation results. © 2019 ACM.