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T–S Fuzzy Modeling for Aircraft Engines: The Clustering and Identification Approach

Author

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  • Muxuan Pan

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

  • Hao Wang

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

  • Jinquan Huang

    (College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China)

Abstract

This paper presents a data-based Takagi-Sugeno (T–S) fuzzy modeling approach for aircraft engines in the flight envelope. We propose a series of T–S fuzzy models for engines with flight conditions as premises and engine linear dynamic models as consequences. By engine dynamic clustering, we determine rough T–S fuzzy models to approximate the nonlinear dynamics of engines in the flight envelope. After that, the maximum–minimum distance-based fuzzy c-means (MMD-FCM) algorithm comes to refine the fuzzy rules and the least square method (LSM) comes to identify premise parameters for each single rough model. The proposed MMD-FCM algorithm guarantees the refined results are stable and reasonable, and the identification improves the accuracy of the steady and transient phases. The model verification showed that the T–S fuzzy models for engines had a high accuracy with a steady error less than 5%, and that the root mean squared error (RMSE) of transient errors was less than 8 × 10 −4 with good generalization ability in the flight envelope.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3284-:d:261134
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    References listed on IDEAS

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    1. Kumar, Mahesh & Patel, Nitin R., 2007. "Clustering data with measurement errors," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6084-6101, August.
    2. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
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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.
    2. 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.
    3. 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).
    4. 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.

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