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.
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BibTeX
@inproceedings{vanDeS:01-04,
author = {van den Boom, Ton J. J. and De Schutter, Bart},
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}
}