Nonlinear Aeroelastic System Identification Based on Neural Network

Bo Zhang,Haiwei Yun,Jinglong Han,Xiaomao Chen

doi:10.3390/app8101916

Abstract

This paper focuses on the nonlinear aeroelastic system identification method based on an artificial neural network (ANN) that uses time-delay and feedback elements. A typical two-dimensional wing section with control surface is modelled to illustrate the proposed identification algorithm. The response of the system, which applies a sine-chirp input signal on the control surface, is computed by time-marching-integration. A time-delay recurrent neural network (TDRNN) is employed and trained to predict the pitch angle of the system. The chirp and sine excitation signals are used to verify the identified system. Estimation results of the trained neural network are compared with numerical simulation values. Two types of structural nonlinearity are studied, cubic-spring and friction. The results indicate that the TDRNN can approach the nonlinear aeroelastic system exactly.

Highlights

Investigation treated the aeroelastic system as purely linear [1]
Many sources of nonlinearity exist in the actual aeroelastic system, such as structural nonlinearity or aerodynamic nonlinearity, both of which affect performance of the system
The results indicate that the identified system approaches the real nonlinear aeroelastic system

Summary

Introduction

Investigation treated the aeroelastic system as purely linear [1]. Many sources of nonlinearity exist in the actual aeroelastic system, such as structural nonlinearity or aerodynamic nonlinearity, both of which affect performance of the system. The effects of three types of structural nonlinearity on the flutter of a two-degree-of-freedom system were calculated on an analog computer [2,3]. Describing functions and harmonic balance were adopted to approach nonlinear flutter problems [4,5,6,7,8,9], as both can be used to predict some nonlinear behavior. The describing function approach does not permit a full exploration of the effect of nonlinear behavior [10]

Objectives

Results

Conclusion