Model predictive control for perturbed max-plus-linear systems: A stochastic approach


Reference:
T.J.J. van den Boom and B. De Schutter, "Model predictive control for perturbed max-plus-linear systems: A stochastic approach," Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida, pp. 4535-4540, Dec. 2001.

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 perturbations-free case and for the case with bounded noise and/or modeling errors. In this paper we extend our previous results on MPC for perturbed max-plus-linear systems to a stochastic setting. We show that under quite general conditions the resulting optimization problems turn out to be convex and can be solved very efficiently.


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Bibtex entry:

@inproceedings{vanDeS:01-04,
        author={T.J.J. {van den Boom} and B. {De Schutter}},
        title={Model predictive control for perturbed max-plus-linear systems: A stochastic approach},
        booktitle={Proceedings of the 40th IEEE Conference on Decision and Control},
        address={Orlando, Florida},
        pages={4535--4540},
        month=dec,
        year={2001}
        }



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