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.
Highlights
The modern aircraft engine is a complicated thermomechanical system which works in the conditions of high temperature, high stress, and variable load; it is difficult to control such plant which belongs to strongly nonlinear, complex, multivariable time-varying systems.[1]
The common approach is splitting the flight envelop into different operating points; the approximated linear models at each operating point are employed in aero-engine controller, such as finite impulse response model[2] or small perturbation state space model.[2,3,4]
The proposed method is applied to the nonlinear component level engine model, state variable engine model, and linear parameter-varying (LPV) engine model; the results show that the performance of the proposed hybrid iterative learning controller is much better than the traditional proportional integral derivative (PID) controller
Summary
The modern aircraft engine is a complicated thermomechanical system which works in the conditions of high temperature, high stress, and variable load; it is difficult to control such plant which belongs to strongly nonlinear, complex, multivariable time-varying systems.[1]. The proposed method is applied to the nonlinear component level engine model, state variable engine model, and linear parameter-varying (LPV) engine model; the results show that the performance of the proposed hybrid iterative learning controller is much better than the traditional PID controller.
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