Sliding Mode Control of Magnetic Levitation System Using Radial Basis Function Neural Networks

Author

Aliasghary, M. ; Jalilvand, A. ; Teshnehlab, M. ; Shoorehdeli, M. Aliyari

Author_Institution

Electr. Eng. Dept., Sci. & Res. Branch of Islamic Azad Univ., Tehran

fYear

2008

fDate

21-24 Sept. 2008

Firstpage

467

Lastpage

470

Abstract

This paper has developed a sliding mode controller (SMC) based on a radial basis function model for control of magnetic levitation system. Adaptive neural networks controllers need plant´s Jacobain, but here this problem solved by sliding surface and generalized learning rule in case to eliminate Jacobain problem. The simulation results show that this method is feasible and more effective for magnetic levitation system control.

Keywords

adaptive control; learning (artificial intelligence); magnetic levitation; neurocontrollers; radial basis function networks; variable structure systems; Jacobain problem; adaptive neural networks controllers; generalized learning rule; magnetic levitation system; radial basis function neural networks; sliding mode control; Adaptive control; Adaptive systems; Coils; Control systems; Jacobian matrices; Magnetic levitation; Neural networks; Programmable control; Radial basis function networks; Sliding mode control; Magnetic levitation system; Radial basis function; Sliding mode; Sliding surface;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics, Automation and Mechatronics, 2008 IEEE Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-1675-2

Electronic_ISBN

978-1-4244-1676-9

Type

conf

DOI

10.1109/RAMECH.2008.4681421

Filename

4681421