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

Muxuan Pan, Hao Wang and Jinquan Huang
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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

Energies, 2019, vol. 12, issue 17, 1-15

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.

Keywords: aircraft engine; T–S fuzzy model; Max-Min distance; Fuzzy-C-Means; least square method; parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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