Volume 14, Issue 3 (Journal of Control, V.14, N.3 Fall 2020)
JoC 2020, 14(3): 103-116 |
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Moaveni B, Shafaati M. Scaling, Modeling and Traffic Control of a Real Railway Network using Max-plus Algebra and Model Predictive Control. JoC 2020; 14 (3) :103-116
URL: http://joc.kntu.ac.ir/article-1-550-en.html
URL: http://joc.kntu.ac.ir/article-1-550-en.html
1- K. N. Toosi University of Technology
2- Iran Univeristy of Science and Technology
2- Iran Univeristy of Science and Technology
Abstract: (7264 Views)
Delay time recovery can increase the efficiency of the railway network and increase the attractiveness of railway transport against other transportation systems. This article presents a new dynamical model of railway system. The proposed model is a discrete event systems that is defined based on the deviation of travel time and deviation of stop time of trains. Due to the existence of multiple substations along the path and the possibility of solving the control problem at an acceptable time, the realization of the real railway network of the Islamic Republic of Iran into a smaller network has been used without losing the integrity of the issue. The Max-Plus is used to model the dynamics of traffic along the route and Model Predictive Control is used to control the traffic. The goals of the control system are reduction of time delay along the route and eliminate it by reducing the train travel time between stations and change in meeting or not meeting time scheduled trains in the interface stations. In the final section of the paper, simulation results are presented to show the validity of the proposed model as well as the successful performance of the MPC system. It should be noted that in this study, modeling and simulations have been done based on actual traffic information in the main lines of the Islamic Republic of Iran Railways.
Keywords: Dynamic Modeling of Railway Traffic, Max-Plus Algebra, Model Predictive Control, Time Delay Recovery.
Type of Article: Research paper |
Subject:
Special
Received: 2017/12/21 | Accepted: 2018/11/23 | ePublished ahead of print: 2019/08/15 | Published: 2020/12/20
Received: 2017/12/21 | Accepted: 2018/11/23 | ePublished ahead of print: 2019/08/15 | Published: 2020/12/20
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