Identification of a non-linear F/A-18 model by the use of fuzzy logic and neural network methods

Abstract

The aim of this article is to determine the mathematical model between control deflections and structural deflections in an F/A-18 modified aircraft in the active aeroelastic wing programme. One future application would be the design of a flutter suppression model based on flight flutter tests. Five excited sources were provided by NASA Dryden Flight Research Center from flight flutter tests. These excitations, given by aircraft control surfaces, are: differential and collective ailerons, collective and differential stabilizers, and rudders. The neural network and fuzzy logic algorithms were chosen in order to identify the multi-input multi-output system for the F/A-18 aircraft. One main contribution of this article is the mapping of fuzzy logic algorithm results into neural network data. Then, these methods were applied for the F/A-18 model identification and validation for sixteen flight conditions expressed in terms of Mach numbers variations between 0.85 and 1.30 and altitudes varying between 5000 and 25 000 ft. Accurate results were obtained, expressed in terms of fit coefficients between estimated and measured signals greater than 99 per cent, which allows one to conclude that these new methodologies are very efficient for an aircraft identification and validation.

Keywords

fuzzy logic neural network model identification flight tests

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