Model predictive control for perturbed max-plus-linear systems


Reference:
T.J.J. van den Boom and B. De Schutter, "Model predictive control for perturbed max-plus-linear systems," Systems & Control Letters, vol. 45, no. 1, pp. 21-33, Jan. 2002.

Abstract:
Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is "linear" in the (max,+) algebra. In our previous work we have only considered MPC for the deterministic noise-free case without modeling errors. In this paper we extend our previous results on MPC for max-plus-linear systems to cases with noise and/or modeling errors. We show that under quite general conditions the resulting optimization problems can be solved very efficiently.


Downloads:
 * Online version of the paper
 * Corresponding technical report: pdf file (214 KB)
      Note: More information on the pdf file format mentioned above can be found here.


Bibtex entry:

@article{vanDeS:00-18,
        author={T.J.J. {van den Boom} and B. {De Schutter}},
        title={Model predictive control for perturbed max-plus-linear systems},
        journal={Systems \& Control Letters},
        volume={45},
        number={1},
        pages={21--33},
        month=jan,
        year={2002},
        doi={10.1016/S0167-6911(01)00162-1}
        }



Go to the publications overview page.


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