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An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

Author

Listed:
  • Ziyu Gu

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Shuwei Pang

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Wenxiang Zhou

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Yuchen Li

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Qiuhong Li

    (Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1255-:d:745168
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    References listed on IDEAS

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    1. Nannan Gu & Xi Wang & Feiqiang Lin, 2019. "Design of Disturbance Extended State Observer (D-ESO)-Based Constrained Full-State Model Predictive Controller for the Integrated Turbo-Shaft Engine/Rotor System," Energies, MDPI, vol. 12(23), pages 1-24, November.
    2. Miao Yu & Xianqiang Yang & Xinpeng Liu, 2021. "LPV system identification with multiple-model approach based on shifted asymmetric laplace distribution," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(7), pages 1452-1465, May.
    3. 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.
    4. Linhai Zhu & Jinfu Liu & Yujia Ma & Weixing Zhou & Daren Yu, 2020. "A Corrected Equilibrium Manifold Expansion Model for Gas Turbine System Simulation and Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    5. Qianjing Chen & Jinquan Huang & Muxuan Pan & Feng Lu, 2019. "A Novel Real-Time Mechanism Modeling Approach for Turbofan Engine," Energies, MDPI, vol. 12(19), pages 1-18, October.
    6. 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.
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    Cited by:

    1. Hongyi Chen & Qiuhong Li & Shuwei Pang & Wenxiang Zhou, 2022. "A State Space Modeling Method for Aero-Engine Based on AFOS-ELM," Energies, MDPI, vol. 15(11), pages 1-15, May.

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