MPC for perturbed max-plus-linear systems

Model predictive control (MPC) is a popular controller design technique in the process industry. Conventional MPC uses (non)linear 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. Up to now we have only considered the deterministic noise-free case without modeling errors. In this paper we extend our previous results to cases with noise and/or modeling errors. We show that under quite general conditions the resulting optimization problem can be solved very efficiently.