Aircraft System Identification Using Parametric Approaches and Intelligent Modeling

Abstract

In this paper, a nonlinear aircraft system is identified using parametric approaches and intelligent modeling. For this, the simulation of the selected aircraft, here the Boeing 747, is performed and then by applying appropriate inputs and stimulating the system modes, the outputs are extracted. To estimate system parameters, aircraft dynamics simulation and then implementation of detection methods are performed. The main purpose of the aircraft system identification is to estimate the aerodynamic force and torque coefficients using parametric identification approaches and intelligent modeling. For the parametric approaches, the least square error and the recursive least square methods are applied and for the intelligent modeling, the artificial neural networks and adaptive neural fuzzy inference system are used. In order to evaluate the accuracy of identification, the estimated aerodynamic force and torque coefficients are compared with the results from the simulation. It is shown that the graphs are almost matched that indicates a low value of outputs difference (error value). The results from parametric approaches and intelligent models indicate appropriate accuracy in identifying the aerodynamic coefficients of the nonlinear aircraft system.