A Study on Aero-Engine Direct Thrust Control with Nonlinear Model Predictive Control Based on Deep Neural Network

  • Qiangang ZhengEmail author
  • Shuwei Pang
  • Haibo Zhang
  • Zhongzhi Hu
Original Paper


For enhancing engine response ability, a novel nonlinear model predictive control (NMPC) method for aero-engine direct thrust control is proposed. The control objective of the proposed method is the thrust instead of the measurable parameters. The online-sliding window deep neural network (OL-SW-DNN) is proposed as predictive model. The OL-SW-DNN adopts deep-learning structure to increase the model accuracy and selects the nearest point data of certain length as training data which will reduce the sensitivity for the noise of training data. The direct thrust simulations of the popular NMPC based on extended Kalman filler (EKF) and the proposed one are conducted, respectively. The simulations demonstrate that compared with the popular NMPC, the proposed NMPC decreases the acceleration time by 0.425 s and increases response speed about 1.14 times.


Aero-engine Nonlinear model predictive control Online Deep neural network 

List of Symbols




Mach number


Power-level angle


Fuel flow


Engine thrust


High-pressure turbine inlet temperature


Fan rotor speed


Compressor rotor speed


Fan surge margin


Compressor surge marge



This was supported in part by the Fundamental Research Funds for the Central Universities under Grant NT2019004, in part by the National Natural Science Foundation of China under Grant 51576096, in part by Q. Lan and the 333 Project, in part by the Research Funds for Central Universities under Grant NF2018003, and in part by Six Talents Peak Project of Jiangsu Province.


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Copyright information

© The Korean Society for Aeronautical & Space Sciences 2019

Authors and Affiliations

  1. 1.Jiangsu Province Key Laboratory of Aerospace Power SystemNanjing University of Aeronautics and AstronauticsNanjingChina

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