Designing an Optimal Automatic Train Operation System Using a Model Predictive Controller with a Case Study of a Part of Tehran Metro Line 1

Document Type : Research Paper

Authors

1 Department of Control Engineering and Signalling, School of Railway Engineering, Iran University of Science and Technology, Tehran, Iran

2 Department of Control Engineering, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

With the increasing demand for intra-city travel and also considering environmental issues, intra-city rail transportation is considered a green solution, an option of double importance. Given the limited infrastructure, including rail lines and fleets and energy resources, safe and optimal operation of rail networks is of particular importance. In this research, after examining the purpose and performance of automatic train guidance technology, an automatic guidance system using a model predictive control method has been designed and introduced. As an innovation of this research, a new predictive control method is presented that, by considering the technical specifications of the train and the rail line, determines and implements a strategy for train movement that, while taking into account the physical limitations of the train and the line, and observing the constraints related to travel safety and efficiency, also considers the issue of energy consumption optimization; Thus, first by examining the dynamics of train motion and the physical relations governing it, state space equations were obtained for more accurate modeling of train motion, and then using a predictive controller, a system for optimal train control was designed. Finally, to investigate the effect of the proposed system and its efficiency in the presence of disturbances and uncertainties related to the practical space, a case study of Tehran Metro Line 1 is conducted by simulating train motion from Shahid Bukharaei Station to the South Terminal Station, and the results indicate a 5.4 percent improvement in fuel consumption and its operational success.

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References

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Journal of Modeling in Engineering
Volume 23, Issue 83
December 2025
Pages 85-101

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History

  • Receive Date: 11 December 2022
  • Revise Date: 12 February 2024
  • Accept Date: 19 February 2024

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