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

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.