Reference
T. J. J. van den Boom, B. De
Schutter, and B. Heidergott, "Complexity reduction in MPC for stochastic
max-plus-linear systems by variability expansion,"
Proceedings
of the 41st IEEE Conference on Decision and Control, Las Vegas, Nevada,
pp. 3567-3572, Dec. 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-plus algebra. In
our previous work we have considered MPC for the perturbations-free case and
for the case with noise and/or modeling errors in a bounded or stochastic
setting. In this paper we consider a method to reduce the computational
complexity of the resulting optimization problem, based on variability
expansion. We show that the computational load is reduced if we decrease the
level of "randomness" in the system.
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BibTeX
@inproceedings{vanDeS:02-007,
author = {van den Boom, Ton J. J. and De Schutter, Bart and Heidergott,
Bernd},
title = {Complexity Reduction in {MPC} for Stochastic Max-Plus-Linear
Systems by Variability Expansion},
booktitle = {Proceedings of the 41st IEEE Conference on Decision and
Control},
address = {Las Vegas, Nevada},
pages = {3567--3572},
month = dec,
year = {2002}
}