Research on self-learning control method for aircraft engine above idle state

Abstract

The iterative learning control for aircraft engine above idle state is studied. An approach combining the proportional integral iterative learning with the traditional proportional integral derivative controller is proposed and then this hybrid iterative learning controller is constructed to control the speed of three typical engine models. In the simulation study, the proposed method is applied to the nonlinear component level engine model, state variable engine model, and linear parameter-varying engine model; the results show that the performance of the proposed hybrid iterative learning controller is much better than the traditional proportional integral derivative controller.