Fuzzy modelling for aircraft dynamics identification

Abstract

A new methodology is described to identify aircraft dynamics and extract the corresponding aerodynamic coefficients. The proposed approach makes use of fuzzy modelling for the identification process where input/output data are first classified by means of the concept of fuzzy clustering and then the linguistic rules are extracted from the fuzzy clusters. The fuzzy rule-based models are in the form of affine Takagi-Sugeno models, that are able to approximate a large class of nonlinear systems. A comparative study is performed with existing techniques based on the employment of neural networks, showing interesting advantages of the proposed methodology both for the physical insight of the identified model and the simplicity to obtain accurate results with fewer parameters to be properly tuned.