Vertical Vibration Reduction of Maglev Vehicles using Nonlinear MPC

TL;DR

This paper introduces a nonlinear model predictive control strategy for Maglev trains that models suspension dynamics to effectively reduce vertical vibrations and improve ride comfort, outperforming existing methods.

Contribution

It presents a novel NMPC approach that explicitly incorporates suspension dynamics, enhancing vibration mitigation and control tuning in high-speed Maglev vehicles.

Findings

Outperforms existing controllers in vibration suppression

Improves passenger comfort and ride quality

Enables better tuning between air gap tracking and ride comfort

Abstract

This work presents a novel Nonlinear Model Predictive Control (NMPC) strategy for high-speed Maglev vehicles that explicitly incorporates mechanical suspension dynamics into the control model. Unlike conventional approaches, which often neglect the interaction between levitation magnet and car body motion, the proposed method enables predictive vibration mitigation by modeling both electromagnetic forces and suspension behavior. This integrated approach significantly improves passenger comfort and ride quality by reducing vertical oscillations caused by track irregularities. Moreover, it allows for a more effective tuning of the trade-off between precise air gap tracking and ride comfort. Simulations based on a detailed multibody model of the Transrapid demonstrate that the method outperforms existing controllers in vibration suppression, making it a promising solution for future high-speed Maglev applications.