Control of the maglev system is one of the most significant technologies of the maglev train. The common proportion integration differentiation (PID) method, which has fixed control parameters, ignores the non-linearity and uncertainty of the model in the design process. In the actual process, due to environmental changes and interference, the inherent parameters of the system will drift significantly. The traditional PID controller has difficulty meeting the control requirements, and will have poor control effect in the actual working environment. Therefore, a radial basis function (RBF)-PID controller is designed in this article, which can use the information from the levitation system identified by the RBF network to adjust the parameters of the controller in real time. Compared with the traditional PID control method, it is shown that the RBF-PID method can improve the control performance of the system through simulation and experiment.

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