Reference
T. J. J. van den Boom and B. De
Schutter, "Model predictive control for perturbed max-plus-linear systems: A
stochastic approach,"
International Journal of Control,
vol. 77, no. 3, pp. 302-309, Feb. 2004.
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 these results on MPC for max-plus-linear systems to a stochastic
setting. We show that under quite general conditions the resulting optimization
problems turns out to be convex and can thus be solved very efficiently.
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BibTeX
@article{vanDeS:02-005,
author = {van den Boom, Ton J. J. and De Schutter, Bart},
title = {Model Predictive Control for Perturbed Max-Plus-Linear Systems:
{A} Stochastic Approach},
journal = {International Journal of Control},
volume = {77},
number = {3},
pages = {302--309},
month = feb,
year = {2004}
}