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.