A Track Step Identification Algorithm for Medium and Low Speed Maglev Trains Based on Fuzzy Theory

The destabilizing effect of track steps on the levitation stability of medium and low speed maglev trains during running has long been a concern. Addressing the real-time identification of track step information is crucial for improving levitation stability of maglev trains. This study begins by analyzing the signal change characteristics of the triple air-gap sensor as the maglev train passes over track joints. Subsequently, fuzzy theory is employed to model the sensor's response to these track joints and divide it into several stages. Using a fuzzy logic system, optimal moments for sampling track step heights are determined, enabling real-time output of the most recent step height data. Simulation results indicate significant reductions in error and result delay. This method offers clear physical interpretations at each part and ensures manageable real-time identification errors and delays, making it suitable for track step identification and optimization control during medium and low speed maglev train running.