Nonlinear Affine System Identification and Feedforward–Feedback Control for Turbofan Engines Based on Polynomial Feature Enhanced Multi-Layer Perceptron

Turbofan engines exhibit complex nonlinear dynamics across the entire flight envelope, which cannot be captured by explicit mathematical models, posing significant challenges for engine controller design. Traditional control designs often rely on multiple linearized models covering the entire operating range, which require designing multiple linear controllers and gain-scheduling strategy to switch these sub-controllers. This article aims to establish a global nonlinear affine model-based feedforward control method for turbofan engines, and the control input can be efficiently obtained through simple algebraic calculations with given reference signal. In this method, the nonlinear affine model via a multi-layer perceptron (MLP) combined with polynomial feature expansion is constructed based on engine model simulation data. With this affine model, the need for the cumbersome design process of multiple linear controllers and their sub-controller switching can be avoided. Additionally, a Proportional–Integral (PI) feedback controller is integrated with the feedforward controller to eliminate tracking errors caused by model mismatches and disturbances. Numerical simulation results verify that the proposed MLP model has high identification accuracy, and the composite control strategy can simplify the control design. During the acceleration/deceleration process between intermediate and idle state, the high-pressure rotor speed overshoot is less than 0.01%, and the settling time is less than 2.6 s, outperforming the 4.1 s of the gain-scheduled PI controllers.