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

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 determin...

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Bibliographic Details
Published in:Energies (Basel) 2019-08, Vol.12 (17), p.3284
Main Authors: Pan, Muxuan, Wang, Hao, Huang, Jinquan
Format: Article
Language:English
Subjects:
Accuracy
Aircraft
aircraft engine
Aircraft engines
Airplane engines
Algorithms
Clustering
Computer simulation
Distributed control systems
Dynamic characteristics
Economic models
Fault diagnosis
Flight
Flight conditions
Flight envelopes
Fuzzy-C-Means
International conferences
least square method
Machine tool industry
Max-Min distance
Model accuracy
Modelling
Net present value
Parameter identification
Researchers
Statistics
Turbofan engines
T–S fuzzy model
Validity
Verification
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12173284