Model predictive control for rail condition-based maintenance: A multilevel approach


Reference:

Z. Su, A. Núñez, S. Baldi, and B. De Schutter, "Model predictive control for rail condition-based maintenance: A multilevel approach," Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, pp. 354-359, Nov. 2016.

Abstract:

This paper develops a multilevel decision making approach based on model predictive control (MPC) for condition-based maintenance of rail. We address a typical railway surface defect called "squat", in which three maintenance actions can be considered: no maintenance, grinding, and replacement. A scenario-based scheme is applied to address the uncertainty in the deterioration dynamics of the key performance indicator for each track section, and a piecewise-affine model is used to approximate the expected dynamics, which is to be optimized by a scenario-based MPC controller at the high level. A static optimization problem involving clustering and mixed integer linear programming is solved at the low level to produce an efficient grinding and replacing schedule. A case study using real measurements obtained from a Dutch railway line between Eindhoven and Weert is performed to demonstrate the merits of the proposed approach.

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Bibtex entry:

@inproceedings{SuNun:16-022,
author={Z. Su and A. N{\'{u}}{\~{n}}ez and S. Baldi and B. {D}e Schutter},
title={Model predictive control for rail condition-based maintenance: {A} multilevel approach},
booktitle={Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)},
address={Rio de Janeiro, Brazil},
pages={354--359},
month=nov,
year={2016},
doi={10.1109/ITSC.2016.7795579}
}



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