A Distributed Model Predictive Control Approach for Virtually Coupled Train Set with Adaptive Mechanism and Particle Swarm Optimization

Zhiyu He, Zhuopu Hou (), Ning Xu, Dechao Liu and Min Zhou
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Zhiyu He: Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Zhuopu Hou: Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Ning Xu: Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Dechao Liu: Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China
Min Zhou: School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2025, vol. 13, issue 10, 1-19

Abstract: Virtual coupling (VC) technology, which determines the safe interval between trains based on relative braking distance, offers a promising solution by enabling tighter yet safe train-following intervals through advanced communication and control strategies. This paper focuses on addressing the virtually coupled train set (VCTS) control problem within the framework of distributed model predictive control (DMPC), in which train dynamics model incorporates uncertainties in basic resistance and control inputs, with an adaptive mechanism (ADM) designed to limit errors caused by external disturbances. A multi-objective cost function is established, considering position error, speed error, and ride comfort, while constraints such as actuator saturation, speed limits, and safe tracking distance are enforced. Particle swarm optimization (PSO) is employed to solve the non-convex optimization problem globally. Simulation experiments validate the effectiveness of the proposed method, demonstrating stable operation of VCTS under various initial conditions and the ability to handle uncertainties through the adaptive mechanism. The results show that the proposed DMPC approach significantly reduces tracking errors and improves ride comfort, highlighting its potential for enhancing railway capacity and operational efficiency.

Keywords: virtual coupling; virtually coupled train set; distributed model predictive control; adaptive control; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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