Abstract:

A controller of a turboshaft engine is designed by the nonlinear model predictive control (NMPC) method. The model predictive control system is mainly comprised of three parts: a predictive model, its online optimization and feedback correction. A neural network model is used to predict the dynamic response of the turboshaft engine and generate the predictive model of the NMPC; the sequential quadratic programming (SQP) optimization algorithm is used to optimize the fuel flow of the turboshaft engine online; the error between the neural network model output and the real engine output is used to correct by feedback, the reference of the controller. Finally, a digital simulation is performed, which shows that compared with the serial PID controller the overshoot of the NMPC controller is reduced from 2.2% to 0.8%, and the response time is reduced from 6 s to 2 s. So the NMPC controller has better control quality.

Key words: turboshaft , engine,  , NMPC,  , SQP,  , neural , network , model,  , feedback , correction