Model Predictive Control for Discrete-Event and Hybrid Systems - Part II: Hybrid Systems

Reference

B. De Schutter and T.J.J. van den Boom, "Model Predictive Control for Discrete-Event and Hybrid Systems - Part II: Hybrid Systems," Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, 10 pp., July 2004. Paper 313.

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 and its companion paper ("Part I: Discrete-Event Systems") we give an overview of some results in connection with 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. After having discussed MPC for max-plus-linear discrete-event systems in the companion paper, we now discuss MPC for some classes of hybrid systems, viz. mixed logical dynamical systems, max-min-plus-scaling systems, and continuous piecewise-affine systems.

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Companion paper

Bibtex entry

@inproceedings{DeSvan:04-004,
author={B. {D}e Schutter and T.J.J. van den Boom},
title={Model Predictive Control for Discrete-Event and Hybrid Systems -- {P}art {II}: {H}ybrid Systems},
booktitle={Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004)},
address={Leuven, Belgium},
month=jul,
year={2004},
note={Paper 313}
}

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