Keywords: high-speed maglev, model predictive control, parameter optimisation, magnetic levitation, sensitivity analysis, weighting coefficients optimization.

The pursuit of sustainable high-speed mobility has positioned magnetic levitation (Maglev) technology, particularly the 600 km/h generation developed by CRRC Qingdao Sifang, at the forefront of modern transportation research. Ensuring stable levitation at such extreme velocities requires control strategies capable of handling severe nonlinearities and stringent safety constraints. Building upon the Model Predictive Control framework previously developed at the Institute of Engineering and Computational Mechanics (ITM), University of Stuttgart, the critical influence of cost function weighting coefficients on the closed-loop performance of the electromagnetic levitation is investigated. While MPC offers superior tracking capabilities compared to classical control schemes, the optimal balance between air-gap precision and ride comfort remains a complex engineering challenge under track irregularities. A systematic sensitivity analysis is conducted through a series of comprehensive numerical simulations, spanning a grid of weighting coefficients for air-gap deviation, magnet current, and acceleration. By leveraging a vehicle-guideway coupled model that accounts for nonlinear dynamics, the inherent trade-off between tracking stiffness and vibration isolation is quantitatively evaluated, providing a robust framework for optimizing the control performance of the electromagnetic levitation system. These findings contribute to the refinement of existing distributed MPC architectures, ensuring both safety margins and operational efficiency for the next generation of high-speed Maglev transit.

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