Yang, Haoyu (Beijing Jiaotong University), Chai, Ming (Beijing Jiaotong University), Su, Youbin (CHN Energy Shuohuang Railway Development Co., Ltd.), He, Zhanyuan (CHN Energy Shuohuang Railway Development Co., Ltd., Suning, Hebe), Liu, Haoyuan (Beijing Jiaotong University), Wang, Hai-Feng (Bijing Jiaotong University)

Automatic Train Operation of Heavy-Haul Trains Based on Model Predictive Control

Scheduled for presentation during the Regular Session "S33c-Intelligent Control for Next-Generation Railway Systems" (FR-LA-T33), Friday, November 21, 2025, 16:00−16:20, Southport 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

The automatic train operation(ATO) of heavy-haul trains has gradually become a development trend. This study establishes a multi-mass dynamic model for 20,000-ton heavy-haul trains, incorporating a circulating air braking strategy. Based on the model predictive control (MPC) algorithm, we designed a speed tracking controller to reduce the coupler force impulse, decrease energy consumption, and improve the accuracy of speed tracking. Finally, based on the real line data of Shuohuang, we conduct simulation experiments on the influence of different prediction steps and weight coefficients on control performance. The algorithm's effectiveness is verified by analysis and comparison, and the optimal parameters for improving performance are found.