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," International Journal of Control, vol. 77, no. 3, pp. 302-309, Feb. 2004.

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 these results on MPC for max-plus-linear systems to a stochastic setting. We show that under quite general conditions the resulting optimization problems turns out to be convex and can thus be solved very efficiently.


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Bibtex entry:

@article{vanDeS:02-005,
        author={T.J.J. {van den Boom} and B. {De Schutter}},
        title={Model predictive control for perturbed max-plus-linear systems: A stochastic approach},
        journal={International Journal of Control},
        volume={77},
        number={3},
        pages={302--309},
        month=feb,
        year={2004},
        doi={10.1080/00207170310001656047}
        }



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