Robust control strategies facing disturbances in Railway Transport Networks

Anis Mhalla; Mohanned Gaied; Dimitri Lefebre

doi:10.17485/IJST/v14i42.1181

Article

Robust control strategies facing disturbances in Railway Transport Networks

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i42.1181

Year: 2021, Volume: 14, Issue: 42, Pages: 3133-3143

Original Article

Robust control strategies facing disturbances in Railway Transport Networks

Anis Mhalla^1*, Mohanned Gaied¹, Dimitri Lefebre²

¹Research Unit LASEE, National Engineering School of Monastir, Avenue Ibn El Jazzar, 5019, Monastir, Tunisia
²GREAH, Univ. Le Havre Normandie, 75, rue Bellot, 76600, Le Havre

*Corresponding Author
Email: [email protected]

Received Date:26 June 2021, Accepted Date:06 November 2021, Published Date:09 December 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: The key objective of this present article is the presentation of a control strategy which consists to compensate the disturbance as soon as it is observed. The main purpose of the proposed control strategy is to guarantee the respect of railway scheduling. The novel strategy allows compensating temporal disruption as soon as it is observed in order to avoid the time constraint violation and to respect planned scheduling. Methods: An algorithm that amends the measured disturbances by compensating the disruption along the mono-synchronized paths is presented. The proposed algorithm allows computing the available control margin for every controllable place. Findings: The main impartial is to compensate partially or totally the disturbance so as to avoid time constraint violation in railway transport networks. In these networks, time may be a significant parameter because it encompasses constraints to avoid adverse events. The temporal constraints which will arise on the railway network may degrade the planed schedules and consequently affect the availability of transportation systems. This leads to many problems in the decision and optimization of the studied transport systems. Novelty/Applications: This paper is devoted to the modeling and robust control of the Tunisian railway network. In this context, a novel robustness strategy is developed to compensate the time disruption in railway networks and to preserve the planned schedule.

Keywords: robustness; control; temporal disturbances; railway networks; Ptime Petri Nets

References

Khadilkar H. Data-Enabled Stochastic Modeling for Evaluating Schedule Robustness of Railway Networks. Transportation Science. 2017;51(4):1161–1176. Available from: https://dx.doi.org/10.1287/trsc.2016.0703
Lusby RM, Larsen J, Bull S. A survey on robustness in railway planning. European Journal of Operational Research. 2018;266(1):1–15. Available from: https://dx.doi.org/10.1016/j.ejor.2017.07.044
Labadi K. Contribution a la modélisation et à l'évaluation de performances des systèmes logistiques à l'aide d'un nouveau modèle de réseaux de Petri stochastiques. Universite de Technologie de Troyes thesis
Barger P, Schön W, Bouali M. A study of railway ERTMS safety with colored Petri nets. In: The European Safety and Reliability Conference ESREL. p. 1303–1309.
Lalouette J, Brinzei N, Malassé O. Evaluation des performances du système de signalisation ferroviaire européen superposé au système français. Congrès Lambda- Mu. 2010;17. Available from: https://hal.archives-ouvertes.fr/hal-00530353/document
Hagalisletto AM, Bjork J, Yu IC, Enger P. Constructing and Refining Large-Scale Railway Models Represented by Petri Nets. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). 2007;37(4):444–460. Available from: https://dx.doi.org/10.1109/tsmcc.2007.897323
Sarkar S, Dutta A. Petri Net based modelling of railway intersection collision avoidance system. 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT). 2016;p. 356–361.
Venkatarangan MJ, Yam Y. Modelling and Simulation of Two Way Railway Traffic Using Extensions to Petri Nets. 2014 8th Asia Modelling Symposium. 2014;p. 97–103.
Jacobs J. Reducing delays by means of computer-aided ‘on-the-spot’rescheduling. WIT Transactions on The Built Environment. 2004;74:603–612.
Pellegrini P, Marliere G, Pesenti R, Rodriguez J. RECIFE-MILP: An Effective MILP-Based Heuristic for the Real-Time Railway Traffic Management Problem. IEEE Transactions on Intelligent Transportation Systems. 2015;16(5):2609–2619. Available from: https://dx.doi.org/10.1109/tits.2015.2414294
Pellegrini P, Marlière G, Rodriguez J. Real time railway traffic management modeling track-circuits. OpenAccess Ser. Informatics. 2012;25:23–34.
Johanna T. Computer-based decision support for railway traffic scheduling and dispatching: A review of models and algorithms. Algorithmic MeThods Model. Optim. Railways. 2006;2. doi: 10.4230/OASIcs.ATMOS.2005.659
Cavone G, Blenkers L, Van Den Boom T, Dotoli M, Seatzu C, De Schutter B. Railway disruption: a bi-level rescheduling algorithm. In: 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE. 2019.
M’halla A, Lefebvre D, Gaied M. Distributed Monitoring Based on P-Time Petri Nets and Chronicle Recognition of the Tunisian Railway Network. Journal of Electrical and Computer Engineering. 2020;2020:1–12. Available from: https://dx.doi.org/10.1155/2020/6265379
Li X, Shou B, Ralescu D. Train Rescheduling With Stochastic Recovery Time: A New Track-Backup Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2014;44(9):1216–1233. Available from: https://dx.doi.org/10.1109/tsmc.2014.2301140
Dollevoet T, Huisman D, Kroon LG, Veelenturf LP, Wagenaar JC. Application of an iterative framework for real-time railway rescheduling. Computers & Operations Research. 2017;78:203–217. Available from: https://dx.doi.org/10.1016/j.cor.2016.08.011
Robey A, Lindemann L, Tu S, Matni N. Learning Robust Hybrid Control Barrier Functions for Uncertain Systems. IFAC-PapersOnLine. 2021;54(5):1–6. Available from: https://dx.doi.org/10.1016/j.ifacol.2021.08.465
Modica G, Praticò S, Laudari L, Ledda A, Fazio SD, Montis AD. Implementation of multispecies ecological networks at the regional scale: analysis and multi-temporal assessment. Journal of Environmental Management. 2021;289:112494. Available from: https://dx.doi.org/10.1016/j.jenvman.2021.112494
Lu H, Ge Z, Song Y, Jiang D, Zhou T, Qin J. A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting. Neurocomputing. 2021;427:169–178. Available from: https://dx.doi.org/10.1016/j.neucom.2020.11.026

Copyright

© 2021 Mhalla et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)