State-constrained dynamic model for operation control of high-speed maglev trains

Based on a state-constrained model predictive control strategy, incorporating relaxation factors into the cost function, we address open-loop instability and excessive vertical dynamic responses for high-speed maglev trains (velocity, acceleration) caused by track irregularities. This approach addresses the open-loop instability and the excessive vertical velocity and acceleration responses caused by track irregularities. Simulation results under various operating conditions demonstrate the effectiveness of the proposed strategy in reducing the maximum vertical responses of the high-speed maglev train compared to an unconstrained approach. Adjusting the prediction time domain and weight parameters analyzes the impact on the suspension state, and thresholds for effective control are determined. The research findings indicate that the proposed strategy, combining relaxation factors and state constraints, offers significant advantages in precise system control, multi-objective optimization of control parameters, and enhanced system adaptability under complex operating conditions. This provides a theoretical foundation and data support for optimizing the stability control of high-speed maglev levitation systems.