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Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
- Publisher
- Springer Journals
- Copyright
- Copyright © 2019 by The Korean Society for Aeronautical & Space Sciences
- Subject
- Engineering; Aerospace Technology and Astronautics; Fluid- and Aerodynamics
- ISSN
- 2093-274X
- eISSN
- 2093-2480
- DOI
- 10.1007/s42405-019-00191-4
- Publisher site
- See Article on Publisher Site
Abstract
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.
Journal
International Journal of Aeronautical & Space Sciences – Springer Journals
Published: Jul 3, 2019