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

Reference

B. De Schutter and T.J.J. van den Boom, "Model Predictive Control for Discrete-Event and Hybrid Systems - Part I: Discrete-Event Systems," Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, 10 pp., July 2004. Paper 312.

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 II: Hybrid Systems") we give an overview of some results in connection with MPC approaches for discrete-event systems and hybrid systems. In general the resulting optimization problems are nonlinear and nonconvex. However, for some classes of discrete-event systems and hybrid systems tractable solution methods exist. In this paper we consider discrete-event systems, i.e., asynchronous systems with event-driven dynamics. In particular, we discuss MPC for a special class of discrete-event systems, viz. max-plus-linear discrete-event systems, for both the noise-free and perturbed case (i.e., with modeling errors and/or noise). In the companion paper we will discuss MPC for some classes of hybrid systems.

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Companion paper

Bibtex entry

@inproceedings{DeSvan:04-003,
author={B. {D}e Schutter and T.J.J. van den Boom},
title={Model Predictive Control for Discrete-Event and Hybrid Systems -- {P}art {I}: {D}iscrete-Event 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 312}
}

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