Model predictive control for perturbed max-plus-linear systems


Reference:

T.J.J. van den Boom and B. De Schutter, "Model predictive control for perturbed max-plus-linear systems," Systems & Control Letters, vol. 45, no. 1, pp. 21-33, Jan. 2002.

Abstract:

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses linear or nonlinear discrete-time models. Recently, we have extended MPC to a class of discrete event systems that can be described by a model that is "linear" in the (max,+) algebra. In our previous work we have only considered MPC for the deterministic noise-free case without modeling errors. In this paper we extend our previous results on MPC for max-plus-linear systems to cases with noise and/or modeling errors. We show that under quite general conditions the resulting optimization problems can be solved very efficiently.

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Bibtex entry:

@article{vanDeS:00-18,
author={T.J.J. van den Boom and B. {D}e Schutter},
title={Model predictive control for perturbed max-plus-linear systems},
journal={Systems \& Control Letters},
volume={45},
number={1},
pages={21--33},
month=jan,
year={2002},
doi={10.1016/S0167-6911(01)00162-1}
}


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