Dynamic Modeling Method for Turbofan Engines Based on Improved NARX Networks

This paper proposes a NARX network-based modeling method to improve the real-time performance of dynamic component-level engine models. This method combines the component-level model with the NARX network black-box modeling technique. It collects training data by solving the common working equation of the component-level model (N-R model) using the Newton–Raphson method and uses a multilayer feedforward neural network to approximate the nonlinear mapping from the input state space to the solution of the common working equation. This approach eliminates the need for iterative computations in solving the common working equation, thus enhancing the real-time performance of the component-level model. Using the N-R model as the benchmark, simulation results show that the NARX network model with embedded component-level modeling has a maximum dynamic error of 0.061% during ground state modeling and 0.36% across the full envelope; the average runtime for full envelope modeling is 0.046 seconds, which is only 4.3% of that of the benchmark model; for step inputs, the component residual during the dynamic process is only 0.02% of that of the benchmark model. These findings demonstrate that the engine model established by this method has high accuracy, good real-time performance, and robustness.