Aero-engine direct thrust control based on nonlinear model predictive control with composite predictive model

Haoyang Chen; Liangliang Li; Qiangang Zheng; Haibo Zhang

doi:10.1515/tjj-2024-0009

Article

Aero-engine direct thrust control based on nonlinear model predictive control with composite predictive model

Haoyang Chen , Liangliang Li , Qiangang Zheng and Haibo Zhang

Published/Copyright: June 27, 2024

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From the journal International Journal of Turbo & Jet-Engines Volume 42 Issue 1

Abstract

A novel NMPC (Nonlinear Model Predictive Control) based on composite predictive model is proposed and applied to direct engine thrust control. To improve the real-time of NMPC, an adaptive composite model based on SVM (State Variable Model), KF (Kalman Filter), and CLM (Component Level Model) is proposed as predictive model. The correction theory is adopted to establish a full envelope adaptive on-board predictive dynamic model and reduce the data storage of predictive model. At each sampling time, the CLM is calculated only once in the proposed NMPC, instead of many times in the popular NMPC based on EKF (extended Kalman filler). Therefore, the proposed NMPC has better real-time performance than the popular one. The simulations that consist of the proposed NMPC, the popular NMPC based on EKF, and the traditional controller PID are conducted. The simulations demonstrate that the proposed NMPC not only has greatly better real time performance than popular NMPC, but also has faster response speed than traditional controller PID.

Keywords: aero-engine; nonlinear model predictive control; full envelope; adaptive dynamic model; Kalman filter

Corresponding author: Qiangang Zheng, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; and Aero-engine Thermal Environment and Structure Key Laboratory of Ministry of Industry and Information Technology, Nanjing, China, E-mail: zhqg@nuaa.edu.cn

Funding source: Aero Engine Corporation of China industry-university-research cooperation project

Award Identifier / Grant number: HFZL2023CXY013

Funding source: Fund of Prospective Layout of Scientific Research for NUAA

Award Identifier / Grant number: ILA220341A22

Award Identifier / Grant number: ILA220371A22

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors state no conflict of interest.
Research funding: This study was supported part by the Aero Engine Corporation of China industry-university-research cooperation project, China (No. HFZL2023CXY013), part by the Fund of Prospective Layout of Scientific Research for NUAA (Nanjing University of Aeronautics and Astronautics), China (Nos. ILA220341A22, ILA220371A22).
Data availability: Not applicable.

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Received: 2024-01-15

Accepted: 2024-06-01

Published Online: 2024-06-27

Published in Print: 2025-03-26

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Articles in the same Issue

https://doi.org/10.1515/tjj-2024-0009

Keywords for this article

aero-engine; nonlinear model predictive control; full envelope; adaptive dynamic model; Kalman filter