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, 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.


Downloads:
 * Online version of the paper
 * Corresponding technical report: pdf file (145 KB)
      Note: More information on the pdf file format mentioned above can be found here.


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},
        address={Rio de Janeiro, Brazil},
        pages={354--359},
        month=nov,
        year={2016},
        doi={10.1109/ITSC.2016.7795579}
        }



Go to the publications overview page.


This page is maintained by Bart De Schutter. Last update: May 4, 2017.