Modeling of gap sensor for high-speed maglev train based on RBF network

Author

Yongzhi, Jing ; Jian, Xiao

Author_Institution

Key Lab. of Magn. Suspension Technol. & Maglev Vehicle, Minist. of Educ., Chengdu, China

fYear

2011

fDate

20-23 March 2011

Firstpage

279

Lastpage

282

Abstract

The gap sensor plays an important role for electromagnetic levitation system which is a critical component of highspeed maglev train. Artificial neural network is a promising area in the development of intelligent sensors. In this paper, we present an model of gap sensor based on radial basis function (RBF) neural network. The proposed model based RBF scheme incorporates intelligence into the sensor. It is revealed from the simulation studies that this gap sensor model can provide correct gap within ±0.3mm error over a range of temperature variations from 20 °C to 80 °C. The experimental results show that the compensated gap signal meets the requirement of levitation control system.

Keywords

intelligent sensors; magnetic levitation; neurocontrollers; radial basis function networks; railway engineering; RBF network; artificial neural network; electromagnetic levitation system; gap sensor modeling; high-speed maglev train; intelligent sensor; levitation control system; radial basis function neural network; temperature 20 C to 80 C; Biological neural networks; Levitation; Mathematical model; Neurons; Radial basis function networks; Temperature; Temperature sensors; High-speed maglev train; RBF network; gap sensor; inverse model;

fLanguage

English

Publisher

ieee

Conference_Titel

Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2011 IEEE International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-61284-910-2

Type

conf

DOI

10.1109/CYBER.2011.6011809

Filename

6011809