A Data-Driven Comprehensive Evaluation Method for Electromagnetic Suspension Maglev Control System

As new advanced vehicles, the safety and stability of electromagnetic suspension maglev trains have always been a subject of concern. This study introduces the improved R index and tau-distance index into the performance evaluation of the suspension control system, respectively assessing the stability of the suspension gap and the smoothness of train operation, combining them with grey relational analysis to achieve data-driven comprehensive evaluation. Furthermore, feasibility tests on the Fenghuang Maglev Express validate the effectiveness and superiority of the comprehensive evaluation method based on measured data. Experimental results demonstrate that the data-driven comprehensive evaluation method, through designing specialized evaluation metrics and increasing assessment dimensions, effectively evaluates the performance of the suspension system control loop. Compared to a traditional error integral comprehensive performance index, it offers greater comprehensiveness and accuracy, along with real-time state-monitoring capabilities.