Model Predictive Control for Discrete-Event and Hybrid Systems
Reference
B. De Schutter and
T.J.J. van den Boom,
"Model Predictive Control for Discrete-Event and Hybrid Systems,"
Tech. report 03-012, Delft Center for Systems and Control, Delft
University of Technology, Delft, The Netherlands, 16 pp., Aug. 2003.
Paper for the Workshop on Nonlinear Predictive
Control (Workshop S-5) at the 42nd IEEE Conference on Decision
and Control, Maui, Hawaii, Dec. 2003.
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 or nonlinear discrete-time models. In this paper we give an
overview of some results in connection with model predictive control
(MPC) approaches for some tractable classes of discrete-event systems
and hybrid systems. In general the resulting optimization problems are
nonlinear and nonconvex. However, for some classes tractable solution
methods exist. In particular, we discuss MPC for max-plus-linear
systems, for mixed logical dynamical systems, and for continuous
piecewise-affine systems.
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Bibtex entry
@techreport{DeSvan:03-012,
author={B. {D}e Schutter and T.J.J. van den Boom},
title={Model Predictive Control for Discrete-Event and Hybrid Systems},
number={03-012},
institution={Delft Center for Systems and Control, Delft University of
Technology},
address={Delft, The Netherlands},
month=aug,
year={2003},
note={Paper for the \emph{Workshop on Nonlinear Predictive Control (Workshop
S-5)} at the 42nd IEEE Conference on Decision and Control, Maui, Hawaii, Dec.\
2003}
}
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Last update: February 21, 2026.