Title :
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
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;
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
DOI :
10.1109/CYBER.2011.6011809
