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.

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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: Extended 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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