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

In this brief, we propose a fuzzy neural network (FNN) modeling approach which is applied for the modeling of gap sensor in the high-speed maglev train. The gap sensor plays an important role for electro-magnetic levitation system which is a critical component of high-speed maglev train. Artificial neural network is a promising area in the development of intelligent sensors. In this paper, we present a model of gap sensor based on fuzzy neural network. The proposed model based fuzzy network scheme incorporates intelligence into the sensor. The fuzzy neural network, as an inverse model compensator if connected in series to the output terminal of the gap sensor, would estimate the correct true gap in a range of temperature after proper training. We trained the network by gradient descent learning algorithm with momentum. It is revealed from the simulation studies that this gap sensor model can provide correct gap within the error less than ±0.4mm over a range of temperature variations from 20 °C to 80°C and within ±0.2mm only considering the work gap 8mm to 12mm. The experimental results show that the compensated gap signal meets the requirement of levitation control system.