Fault Detection and Diagnosis for GTM UAV with Dual Unscented Kalman Filter
نویسندگان
چکیده
This paper presents an applicable procedure for Fault Detection and Diagnosis (FDD) in a realistic nonlinear six degree-of-freedom unmanned aerial vehicle (UAV) model. The work has been developed based on the Matlab/Simulink environment of the NASA Generic Transport Model (GTM) UAV under the NASA Aviation Safety Program (AvSP). By introducing the partial loss fault in aircraft actuators into the GTM model, the dual Unscented Kalman Filter (UKF) algorithm is implemented for online estimation of both flight states and fault parameters, and for making statistical decisions associated with fault detection and diagnosis.
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