Model predictive control for discrete-event systems

Model predictive control (MPC) is a very popular controller design method in the process industry. An important advantage of MPC is that it allows the inclusion of constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. In this project we extend MPC to a class of discrete-event systems. Typical examples of discrete-event systems are: flexible manufacturing systems, telecommunication networks, traffic control systems, multiprocessor operating systems, and logistic systems. In general models that describe the behavior of a discrete-event system are nonlinear in conventional algebra. However, there is a class of discrete-event systems - the max-plus-linear discrete-event systems - that can be described by a model that is "linear" in the max-plus algebra.

We have further developed our MPC framework for max-plus-linear discrete-event systems and included the influences of noise and disturbances. In addition, we have also extended our results to discrete-event systems that can be described by models in which the operations maximization, minimization, addition and scalar multiplication appear, and to discrete-event systems with both hard and soft synchronization constraints.

Our current research in this context is focus on developing efficient algorithms for MPC for the classes of discrete-event systems described above, and on extending the approach to other classes of discrete-event systems.

Project members: T.J.J. van den Boom (Ton), B. De Schutter (Bart)

Discrete-event systems, Model predictive control, Hybrid and nonlinear systems