Dynamic Modeling and Adaptive Sliding Mode Control for a Maglev Train System Based on a Magnetic Flux Observer

The control method and dynamic performance of a magnetic suspension system, which is the core component of maglev trains, have a significant influence on the performance of the maglev train. Currently, a control strategy based on the current feedback is widely used. However, the stable range of control parameters is relatively small, making it difficult to identify stable regions for the control parameters. In addition, the strong interactions between the control parameters are cause issues in the control. In this paper, a control strategy based on the flux density observer is proposed using an analysis of the working principle and the structure of the suspension system. A nonlinear dynamic model of a maglev system is established by utilizing the state equation of the flux feedback. Based on the current and voltage feedback, a hybrid magnetic flux density observer is presented. According to the mathematical model, an adaptive sliding mode controller is designed to reduce the upper bound of both uncertainty and interference of the sliding mode controller. Finally, a theoretical analysis and the effectiveness of the proposed control strategy are verified using both simulations and experiments.