IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i11p3903-d823799.html
   My bibliography  Save this article

A State Space Modeling Method for Aero-Engine Based on AFOS-ELM

Author

Listed:
  • Hongyi Chen

    (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)

  • 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)

Abstract

State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and cannot be carried out in real time, an aero-engine state space modeling method based on adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed in this paper. The structure of the extreme learning machine (ELM) is determined according to the form of the state space model, and the inverse-free ELM algorithm is used to automatically select the appropriate number of hidden nodes to improve the efficiency of offline initialization. The focus of the ELM on current operation performance is enhanced by the adaptive renewed forgetting factor, which reduces the impact of aero-engine history and deviated data on the current output and improves the accuracy of the model. Then, according to the analytical equation of the ELM model, the state space model of an aero-engine at each sampling time is obtained by using the partial derivative method. The simulation results based on engine test data show that the real-time performance and accuracy of the state space model established online in this paper can meet the needs of aero-engine control system requirement.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3903-:d:823799
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/11/3903/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/11/3903/
    Download Restriction: no
    ---><---

    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Liu, Fan & Chen, Mou & Li, Tao, 2022. "Resilient H∞ control for uncertain turbofan linear switched systems with hybrid switching mechanism and disturbance observer," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    3. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3903-:d:823799. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.