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

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發表在:Mathematics vol. 13, no. 10 (2025), p. 1641
主要作者: He, Zhiyu
其他作者: Hou Zhuopu, Xu, Ning, Liu, Dechao, Zhou, Min
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MDPI AG
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100 1 |a He, Zhiyu  |u Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China; hezhiyu@rails.cn (Z.H.); xuning@rails.cn (N.X.); liudechao@rails.cn (D.L.) 
245 1 |a A Distributed Model Predictive Control Approach for Virtually Coupled Train Set with Adaptive Mechanism and Particle Swarm Optimization 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Speed limits 
653 |a Trains 
653 |a Velocity 
653 |a Particle swarm optimization 
653 |a Cost function 
653 |a Communication 
653 |a Convexity 
653 |a Initial conditions 
653 |a Optimization 
653 |a Controllers 
653 |a Predictive control 
653 |a Error reduction 
653 |a Monitoring systems 
653 |a Tracking errors 
653 |a Uncertainty 
653 |a Actuators 
653 |a Efficiency 
653 |a Passenger comfort 
653 |a Position errors 
700 1 |a Hou Zhuopu  |u Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China; hezhiyu@rails.cn (Z.H.); xuning@rails.cn (N.X.); liudechao@rails.cn (D.L.) 
700 1 |a Xu, Ning  |u Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China; hezhiyu@rails.cn (Z.H.); xuning@rails.cn (N.X.); liudechao@rails.cn (D.L.) 
700 1 |a Liu, Dechao  |u Signal and Communication Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China; hezhiyu@rails.cn (Z.H.); xuning@rails.cn (N.X.); liudechao@rails.cn (D.L.) 
700 1 |a Zhou, Min  |u School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China; zhmin@bjtu.edu.cn 
773 0 |t Mathematics  |g vol. 13, no. 10 (2025), p. 1641 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3212074198/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch 
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