RBF neural network controller based on OLSSVR

Author

Ucak, Kemal ; Oke, Gulay

Author_Institution

Dept. of Control Eng., Istanbul Tech. Univ., Istanbul, Turkey

fYear

2013

fDate

23-26 June 2013

Firstpage

1

Lastpage

6

Abstract

In this paper, a predictive adaptation method based on Online Least Square Support Vector Regression (OLSVR) for a RBF controller has been proposed. System Jacobian is approximated via Online LSSVR model of the system to tune RBF controller. The parameters of the controller have been tuned depending on K-step ahead future behavior of the system to provide adaptation ability to the controller under changing conditions. Levenberg Marquard algorithm is utilized as learning algorithm for controller parameters. The proposed method has been evaluated by simulations carried out on a magnetic levitation system, and the results show that the control performance has been improved.

Keywords

control engineering computing; learning (artificial intelligence); least mean squares methods; magnetic levitation; magnetic variables control; neurocontrollers; predictive control; radial basis function networks; regression analysis; support vector machines; K-step ahead future behavior; Levenberg Marquard algorithm; RBF controller tuning; RBF neural network controller; adaptation ability; learning algorithm; magnetic levitation system; online LSSVR model; online least square support vector regression; predictive adaptation method; Adaptation models; Artificial neural networks; Jacobian matrices; Magnetic levitation; Noise measurement; Support vector machines; Vectors; Levenberg Marquard; Magnetic Levitation; Online Support Vector Regression; RBF Neural Network Controller;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ASCC), 2013 9th Asian

Conference_Location

Istanbul

Print_ISBN

978-1-4673-5767-8

Type

conf

DOI

10.1109/ASCC.2013.6606293

Filename

6606293