Model predictive control for railway networks


Reference:
B. De Schutter and T. van den Boom, "Model predictive control for railway networks," Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM'01), Como, Italy, pp. 105-110, July 2001.

Abstract:
Model predictive control (MPC) is a very popular controller design method in the process industry. Usually MPC uses linear discrete-time models. In this paper we extend MPC to a class of discrete-event systems with both hard and soft synchronization constraints. Typical examples of such systems are railway networks, subway networks, and other logistic operations. In general the MPC control design problem for these systems leads to a nonlinear non-convex optimization problem. We also show that the optimal MPC strategy can be computed using an extended linear complementarity problem.


Downloads:
 * Corresponding technical report: pdf file (190 KB)
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Bibtex entry:

@inproceedings{DeSvan:00-17,
        author={B. {De Schutter} and T. {van den Boom}},
        title={Model predictive control for railway networks},
        booktitle={Proceedings of the 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM'01)},
        address={Como, Italy},
        pages={105--110},
        month=jul,
        year={2001}
        }



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