Model predictive control for perturbed max-plus-linear systems

If you want to cite this report, please use the following reference instead: T.J.J. van den Boom and B. De Schutter, " Model predictive control for perturbed max-plus-linear systems, " 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.