Model Predictive Control for Max-Plus-Linear Discrete-Event Systems:
Extended Report & Addendum
Reference
B. De Schutter and
T. van den Boom,
"Model Predictive Control for Max-Plus-Linear Discrete-Event Systems:
Extended Report & Addendum," Tech. report bds:99-10a, Control
Systems Engineering, Fac. of Information Technology and Systems, Delft
University of Technology, Delft, The Netherlands, 27 pp., Nov. 2000. A
short version of this report has been published in Automatica, vol. 37, no. 7, pp. 1049-1056, July
2001.
Abstract
Model predictive control (MPC) is a very popular controller design
method in the process industry. A key advantage of MPC is that it can
accommodate constraints on the inputs and outputs. Usually MPC uses
linear discrete-time models. In this report we extend MPC to a class
of discrete-event systems that can be described by models that are
"linear" in the max-plus algebra, which has maximization and addition
as basic operations. In general the resulting optimization problem are
nonlinear and non-convex. However, if the control objective and the
constraints depend monotonically on the outputs of the system, the
model predictive control problem can be recast as problem with a
convex feasible set. If in addition the objective function is convex,
this leads to a convex optimization problem, which can be solved very
efficiently.
Downloads
Original paper
Bibtex entry
@techreport{DeSvan:99-10a,
author={B. {D}e Schutter and T. van den Boom},
title={Model Predictive Control for Max-Plus-Linear Discrete-Event Systems:
{E}xtended Report \& Addendum},
number={bds:99-10a},
institution={Control Systems Engineering, Fac.\ of Information Technology and
Systems, Delft University of Technology},
address={Delft, The Netherlands},
month=nov,
year={2000},
note={A short version of this report has been published in \emph{Automatica},
vol.\ 37, no.\ 7, pp.\ 1049--1056, July 2001}
}
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Last update: February 21, 2026.