Dynamic Modeling of Aeroengine Rotor Speed Based on Data Fusion Method

In this paper, a data-driven system identification method is presented based on the data fusion of a dynamic model and flight test data. The dynamic model is built by a combination of nonlinear auto-regressive networks (NARX) and the steady-state model. In such a combination, NARX can calibrate the dynamic characteristics of high-pressure and low-pressure rotor speed based on automatic control system steady-state models. As such, the calibrated engine model’s output speed is able to meet the requirements of simulation test tolerance accuracy. To enhance the robustness of the dynamic model against measurement noise, the Kalman filter is used to fuse the model prediction and the measurement data with noise. As such, the fused model can efficiently remove the influence of measurement noise and improve prediction accuracy. The proposed method supports the construction of reliable and environment-adaptive platforms for simulation application verification and provides high-fidelity simulation incentives for the realization of simulation test scenarios in the aviation industry.