An improved hybrid Kalman filter design for aircraft engine based on a velocity-based LPV framework

In-flight aircraft engine performance estimation is one of the key techniques for advanced intelligent engine control and in-flight fault detection, isolation and accommodation. This paper expounds the current performance degradation estimation methods, and an improved hybrid Kalman filter (HKF) via velocity-based LPV (VLPV) framework is proposed in this paper. Composed of a nonlinear on-board engine model (OBEM) and VLPV, the filter is a hybrid architecture. The outputs of OBEM are used for the baseline of the VLPV Kalman filter, while the system performance degradation factors on-line estimated by the measured real system output deviations are fed back to the OBEM for its updating. In addition, the setting of the process and measurement noise covariance matrices' values are also discussed. By applying it to a commercial turbofan engine, simulation results show that this model can effectively estimate the real engine performance in the whole flight envelope and in different engine states.

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