Research on Decoupling Problem of Suspension Gap and Location of Relative Position Sensor in High Speed Maglev Train

Abstract

The relative position sensor of a high-speed maglev train is an important part of train location and speed detection for motor traction, but its output signal is not only related to position but also related to the suspension gap of the maglev train. The fluctuation of the suspension gap will affect the amplitude of the output signal of the sensor (i.e., the suspension wave signal is coupled with the output signal). The prediction normalization method currently used can eliminate the effect of the suspension fluctuation to a certain extent, but there is a limitation. Aiming at this problem, this paper analyzes the frequency characteristics of the suspension gap fluctuation caused by track irregularities and proposes a gap estimation algorithm based on the adaptive filter. The Kalman filter is used to estimate the gap and then the output signal is compensated according to the numerical relationship between the gap and the output signal of the sensor, so as to achieve the decoupling between the gap and the position measurement. Finally, the effectiveness of the method is proved by the comparison experiments.

Keywords

• High speed maglev train
• relative position sensor
• adaptive filter
• Kalman filter
• normalization