Research on the Active Control of the Train’s Primary Vertical Damper Based on Model Predictive Control

To address the adaptability limitations of passive suspension systems in railway vehicles, this study investigates active control technology for dampers using a locomotive's vertical suspension as the research object. A six-degree-of-freedom vertical dynamics model was established as the recognition framework for the Model Predictive Control (MPC) algorithm. Based on this model, a prediction model was developed, and an objective function was designed with vehicle dynamics response as the constraint to complete the MPC controller design. A joint simulation model integrating the locomotive dynamics model and the MPC controller was constructed in MATLAB, adopting a parallel configuration of active and passive vertical dampers in the pendant suspension scheme.Comparative analysis through joint simulation demonstrated the superiority of active control over passive control in ride comfort. At 60 km/h, the MPC-based active damper control reduced the Sperling stability index by 11.21%, while the peak and RMS values of carbody settlement acceleration decreased by 24.9% and 28.7%, respectively. At 160 km/h, the corresponding reductions reached 32.5% (peak) and 31% (RMS) for settlement acceleration, with a 16.10% improvement in the Sperling stability index. These results validate the effectiveness of MPC-based active control in suppressing vertical vibration and enhancing railway vehicle ride quality.