Lyu, Jiahui (Beijing Jiaotong University), Lu, Wanli (Beijing Jiaotong University), Luo, Zhengwei (Beijing Jiaotong University), Zhao, Zixu (Beijing JiaoTong University), Liu, Hongjie (Beijing Jiaotong University), Lv, Jidong (Beijing Jiaotong University), Chen, Junqiang (Traffic Control Technology Co., Ltd)

Improved Lexicographic Multi-Objective Model Predictive Tracking Control for Train Platoons with Switching Priority

Scheduled for presentation during the Regular Session "S33c-Intelligent Control for Next-Generation Railway Systems" (FR-LA-T33), Friday, November 21, 2025, 16:20−16:40, 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

High-speed train platoon exhibits characteristics of multi-agent cooperative interaction, offering potential for enhanced operational efficiency and intelligent control in modern railway systems. However, achieving safe, energy efficient, and comfortable cooperative control of train platoon has significant challenges due to high speed, complex environment, and stringent constraints. This paper proposes a distributed model predictive control method based on an improved switching lexicographic multi-objective optimization framework. The method constructs a hierarchical cost function based on the priority requirements of the train platoon at different operational stages, transforming the multi-objective model predictive control problem into a dictionary optimization problem. By solving the single-objective optimization problems at each hierarchical level while ensuring train safety constraints, the optimal control strategy is obtained. The effectiveness of the method is validated using real-world data from the Fuxin Station–Heishan North Station high-speed railway. Simulation results demonstrate that, compared with the weight-switching strategy in MPC, the proposed method improves energy efficiency by approximately 30% and enhances ride comfort by around 10%. The proposed method achieves superior speed tracking and inter-train distance regulation while strictly ensuring safety.