Model predictive control for max-plus-linear discrete event systems


Reference:
B. De Schutter and T. van den Boom, "Model predictive control for max-plus-linear discrete event systems," Automatica, vol. 37, no. 7, pp. 1049-1056, July 2001.

Abstract:
Model predictive control (MPC) is a very popular controller design method in the process industry. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. In this paper we extend MPC to a class of discrete-event systems that can be described by models that are "linear" in the max-plus algebra, which has maximization and addition as basic operations. In general the resulting optimization problem are nonlinear and nonconvex. However, if the control objective and the constraints depend monotonically on the outputs of the system, the model predictive control problem can be recast as problem with a convex feasible set. If in addition the objective function is convex, this leads to a convex optimization problem, which can be solved very efficiently.


Downloads:
 * Online version of the paper
 * Corresponding technical report: pdf file (186 KB)
      Note: More information on the pdf file format mentioned above can be found here.


Bibtex entry:

@article{DeSvan:99-10,
        author={B. {De Schutter} and T. {van den Boom}},
        title={Model predictive control for max-plus-linear discrete event systems},
        journal={Automatica},
        volume={37},
        number={7},
        pages={1049--1056},
        month=jul,
        year={2001},
        doi={10.1016/S0005-1098(01)00054-1}
        }



Go to the publications overview page.


This page is maintained by Bart De Schutter. Last update: December 15, 2015.