Identification of nonlinear aeroelastic system using fuzzy wavelet neural network

This paper presents a novel fuzzy wavelet neural network structure to identify the nonlinear uncertain aeroelastic system. The identified aeroelastic system considers stiffness, damping non-linearity, dead zones and uncertainties. The proposed fuzzy wavelet neural network (FWNN) is developed from the interval type-2 fuzzy logic system which has the advantage to model uncertainties. Additionally, taking the rapidity and accuracy of the identification into account, the system is characterized by a set of fuzzy IF-THEN rules, and the consequent parts of which is designed to be single hidden layer wavelet neural network. And then, the sliding mode algorithm based on Lyapunov stability theory is introduced to obtain parameter update rules. Furthermore, numerical simulation for a structurally nonlinear prototypical two-dimensional wing section is investigated to verify the effectiveness of the proposed method.