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Control-Oriented Modeling for Nonlinear MIMO Turbofan Engine Based on Equilibrium Manifold Expansion Model

Author

Listed:
  • Chengkun Lv

    (School of Energy Science and Engineering, Harbin Institute of Technology, No. 92, West Da-Zhi Street, Harbin 150001, China)

  • Ziao Wang

    (School of Energy Science and Engineering, Harbin Institute of Technology, No. 92, West Da-Zhi Street, Harbin 150001, China)

  • Lei Dai

    (Shenyang Airplane Design and Research Institution, Shenyang 110013, China)

  • Hao Liu

    (Xi’an Aerospace Propulsion Institute, Xi’an 710199, China)

  • Juntao Chang

    (School of Energy Science and Engineering, Harbin Institute of Technology, No. 92, West Da-Zhi Street, Harbin 150001, China)

  • Daren Yu

    (School of Energy Science and Engineering, Harbin Institute of Technology, No. 92, West Da-Zhi Street, Harbin 150001, China)

Abstract

This paper investigates the control-oriented modeling for turbofan engines. The nonlinear equilibrium manifold expansion (EME) model of the multiple input multiple output (MIMO) turbofan engine is established, which can simulate the variation of high-pressure rotor speed, low-pressure rotor speed and pressure ratio of compressor with fuel flow and throat area of the nozzle. Firstly, the definitions and properties of the equilibrium manifold method are presented. Secondly, the steady-state and dynamic two-step identification method of the MIMO EME model is given, and the effects of scheduling variables and input noise on model accuracy are discussed. By selecting specific path, a small amount of dynamic data is used to identify a complete EME model. Thirdly, modeling and simulation at dynamic off-design conditions show that the EME model has model accuracy close to the nonlinear component-level (NCL) model, but the model structure is simpler and the calculation is faster than that. Finally, the linearization results are obtained based on the properties of the EME model, and the stability of the model is proved through the analysis of the eigenvalues, which all have negative real parts. The EME model constructed in this paper can meet the requirements of real-time simulation and control system design.

Suggested Citation

  • Chengkun Lv & Ziao Wang & Lei Dai & Hao Liu & Juntao Chang & Daren Yu, 2021. "Control-Oriented Modeling for Nonlinear MIMO Turbofan Engine Based on Equilibrium Manifold Expansion Model," Energies, MDPI, vol. 14(19), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6277-:d:648671
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    References listed on IDEAS

    as
    1. Muxuan Pan & Hao Wang & Jinquan Huang, 2019. "T–S Fuzzy Modeling for Aircraft Engines: The Clustering and Identification Approach," Energies, MDPI, vol. 12(17), pages 1-15, August.
    2. Jinfu Liu & Yujia Ma & Linhai Zhu & Hui Zhao & Huanpeng Liu & Daren Yu, 2020. "Improved Gain Scheduling Control and Its Application to Aero-Engine LPV Synthesis," Energies, MDPI, vol. 13(22), pages 1-18, November.
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    Citations

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    Cited by:

    1. Ziyu Gu & Shuwei Pang & Wenxiang Zhou & Yuchen Li & Qiuhong Li, 2022. "An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines," Energies, MDPI, vol. 15(4), pages 1-19, February.
    2. Lingfei Xiao & Yushuo Tan & Robert R. Sattarov & Ye Wei, 2024. "Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines," Energies, MDPI, vol. 17(18), pages 1-16, September.
    3. Lv, Chengkun & Lan, Zhu & Wang, Ziao & Chang, Juntao & Yu, Daren, 2024. "Intelligent ammonia precooling control for TBCC mode transition based on neural network improved equilibrium manifold expansion model," Energy, Elsevier, vol. 288(C).

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