LAUSR

Abstract The precise control of turbofan engines thrust is an important guarantee for an aircraft to obtain good flight performance and a challenge due to complex nonlinear dynamics of engines and time-varying parameters. The main difficulties lie in the following two aspects. Firstly, it is hard to obtain an accurate kinetic model for the turbofan engine. Secondly, some model parameters often change in different flight conditions and states and even fluctuate sharply in some cases. These variable parameters bring huge challenge for the turbofan engine control. To solve the turbofan engine control problem, this paper presents a non-affine parameter-dependent Linear Parameter Varying (LPV) model-based adaptive control approach. In this approach, polynomial-based LPV modeling method is firstly employed to obtain the basis matrices, and then the Radial Basis Function Neural Networks (RBFNN) is introduced for the online estimation of the non-affine model parameters to improve the simulation performance. LPV model-based Linear Matrix Inequality (LMI) control method is applied to derive the control law. A robust control term is introduced to fix the estimation error of the nonlinear time-varying model parameters for better control performance. Finally, the Lyapunov stability analysis is performed to ensure the asymptotical convergence of the closed loop system. The simulation results show that the states of the engine can change smoothly and the thrust of the engine can accurately follow the desired trajectory, indicating that the proposed control approach is effective. The contribution of this work lies in the combination of linear system control and nonlinear system control methods to design an effective controller for the turbofan engine and to provide a new way for turbofan engine control research.