Application of RBF Network in System Identification for Flight Control Systems

Author

Yijun, Huang ; Wu, Niu

Author_Institution

First Aeronaut. Coll. of Air Force, Xinyang, China

Volume

2

fYear

2010

fDate

16-18 July 2010

Firstpage

67

Lastpage

69

Abstract

The flight control system (FCS) is a complex nonlinear multi-input and multi-output) system, it is very difficult to identify the model of this system. The nonlinear relation of I/O data can be expressed by artificial neural network (ANN). The ANN can fit the any function accurately by studying. In this paper, the FCS of a type of fighter is identified by radial basis function (RBF) network. The training algorithm of the RBF network is improved by a grouping optimizing method and a new training algorithm. Simulation results about application of this network with new algorithm were given. The results show that the complex FCS can be identified by artificial neural network accurately.

Keywords

MIMO systems; aerospace control; large-scale systems; neurocontrollers; nonlinear control systems; optimisation; radial basis function networks; I/O data; RBF network; artificial neural network; complex nonlinear multiinput and multioutput system; flight control system; grouping optimizing method; radial basis function network; system identification; training algorithm; Aerospace control; Artificial neural networks; Estimation; Radial basis function networks; Simulation; System identification; Training; Radial basis functions; flight control system; neural network; system identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology and Applications (IFITA), 2010 International Forum on

Conference_Location

Kunming

Print_ISBN

978-1-4244-7621-3

Electronic_ISBN

978-1-4244-7622-0

Type

conf

DOI

10.1109/IFITA.2010.264

Filename

5634921