Model Predictive Control for Perturbed Max-Plus-Linear Systems: A
Stochastic Approach
Reference
T.J.J. van den Boom and
B. De Schutter,
"Model Predictive Control for Perturbed Max-Plus-Linear Systems: A
Stochastic Approach," Proceedings of the 40th IEEE
Conference on Decision and Control, Orlando, Florida, pp.
4535-4540, Dec. 2001.
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 perturbations-free case and for the case with
bounded noise and/or modeling errors. In this paper we extend our
previous results on MPC for perturbed max-plus-linear systems to a
stochastic setting. We show that under quite general conditions the
resulting optimization problems turn out to be convex and can be
solved very efficiently.
Downloads
- Corresponding technical report:
pdf
file
(301 KB)
Bibtex entry
@inproceedings{vanDeS:01-04,
author={T.J.J. van den Boom and B. {D}e Schutter},
title={Model Predictive Control for Perturbed Max-Plus-Linear Systems: {A}
Stochastic Approach},
booktitle={Proceedings of the 40th IEEE Conference on Decision and Control},
address={Orlando, Florida},
pages={4535--4540},
month=dec,
year={2001}
}
This page is maintained by Bart De Schutter.
Last update: February 21, 2026.