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A method is proposed to detect and identify two common classes of actuator faults in nonlinear systems. The two fault classes are total and partial actuator faults. This is accomplished by representing the nonlinear system by a Linear Parameter Varying (LPV) model, which is derived from experimental input-output data. The LPV model is used in a Kalman filter to estimate augmented states, which are directly related to the faults. Decision logic has been developed to determine the fault class from the estimated augmented states. The proposed method has been validated on a nonlinear simulation model of a small commercial aircraft.

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