Model Predictive Control of Disturbed Maglev System

To improve the suspension control effect of the high-speed maglev train, a model predictive control (MPC) algorithm considering the disturbance force is designed based on the electromagnet suspension system unit of the maglev train.When constructing MPC algorithms, the influence of disturbance force on the system is commonly disregarded.However, neglecting this effect may lead to the failure of the predictive model and subsequently impact the performance of the control algorithm.In the paper, the disturbance force is incorporated as input to establish the state prediction model of the suspension system.The predicted values are then corrected by the error between the actual and the predicted state.Subsequently, the objective function and relevant constraints for the suspension system are designed, and the optimal control quantity of the system is obtained by rolling optimization.Finally, the performance of the MPC controller is evaluated through numerical calculations.Research results indicate that, in comparison with the traditional proportionalintegral-derivative (PID) feedback controller, the MPC controller proposed effectively suppresses the fluctuation of the suspension gap.Moreover, incorporating the disturbance force into the predictive model of the system's state can further optimize the performance of the MPC controller.