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Qiangang Zheng,Shuwei Pang,Haibo Zhang,Zhongzhi Hu 한국항공우주학회 2019 International Journal of Aeronautical and Space Sc Vol.20 No.4
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.