Model predictive control for discrete-event and hybrid systems

Reference:
B. De Schutter and T.J.J. van den Boom, "Model predictive control for discrete-event and hybrid systems," Tech. rep. 03-012, Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands, 16 pp., Aug. 2003. Paper for the Workshop on Nonlinear Predictive Control (Workshop S-5) at the 42nd IEEE Conference on Decision and Control, Maui, Hawaii, Dec. 2003.

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 or nonlinear discrete-time models. In this paper we give an overview of some results in connection with model predictive control (MPC) approaches for some tractable classes of discrete-event systems and hybrid systems. In general the resulting optimization problems are nonlinear and nonconvex. However, for some classes tractable solution methods exist. In particular, we discuss MPC for max-plus-linear systems, for mixed logical dynamical systems, and for continuous piecewise-affine systems.