Adaptive Model Predictive Control Using Max-Plus-Linear Input-Output
Models
Reference
T.J.J. van den Boom,
B. De Schutter,
G. Schullerus, and
V. Krebs,
"Adaptive Model Predictive Control Using Max-Plus-Linear Input-Output
Models," Proceedings of the 2003 American Control
Conference, Denver, Colorado, pp. 933-938, June 2003.
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 time-invariant case. In this paper we consider
an adaptive scheme for the time-varying case, based on parameter
estimation of input-output models. In a simulation example we show
that the combined parameter-estimation/MPC algorithm gives a good
closed-loop behaviour.
Downloads
- Corresponding technical report:
pdf
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(306 KB)
Bibtex entry
@inproceedings{vanDeS:02-021,
author={T.J.J. van den Boom and B. {D}e Schutter and G. Schullerus and V.
Krebs},
title={Adaptive Model Predictive Control Using Max-Plus-Linear Input-Output
Models},
booktitle={Proceedings of the 2003 American Control Conference},
address={Denver, Colorado},
pages={933--938},
month=jun,
year={2003}
}
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Last update: February 21, 2026.