LAUSR

During recent years, maglev transportation has made great progress, and as a result, many intelligent levitation control algorithms have emerged. However, enterprises often find it difficult to make a choice when faced with the selection of a controller. The main reason is that the performance evaluation of control algorithms is a complex, multiple-criteria, multifactor coupling problem that cannot be represented by a precise mathematic model. In this paper, a novel artificial intelligent evaluation method for the selection of a levitation controller is developed based on a 3-grade fuzzy method and analytic hierarchy process (AHP). Three kinds of intelligent levitation control algorithms are applied to a full-size test maglev train to collect experimental results with real data. The proposed artificial intelligence method to develop a 3-grade fuzzy multicriteria approach is used to select the best levitation controller for the maglev train. This method can then provide information consultation services to maglev train firms. To the best of our knowledge, for maglev trains, this is the first intelligent evaluation approach with real experimental data. The proposed method can also be applied to other information consultation and decision making systems with appropriate modifications.