Model predictive control for max-plus-linear discrete event systems

Model predictive control for max-plus-linear discrete event systems * B. De Schutter and T. van den Boom If you want to cite this report, please use the following reference instead: B. De Schutter and T. van den Boom, " Model predictive control for max-plus-linear discrete event systems, " The conventional model predictive control framework is extended and adapted to max-plus-linear systems, i.e., discrete-event systems that can be described by models that are " linear " in the (max,+) algebra. Abstract Model predictive control (MPC) is a very popular controller design method in the process industry. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. In this paper we extend MPC to a class of discrete-event systems that can be described by models that are " linear " in the max-plus algebra, which has maximization and addition as basic operations. In general the resulting optimization problem are non-linear and nonconvex. However, if the control objective and the constraints depend monotonically on the outputs of the system, the model predictive control problem can be recast as problem with a convex feasible set. If in addition the objective function is convex, this leads to a convex optimization problem, which can be solved very efficiently.