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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