Model predictive control for discrete-event and hybrid systems - Part II: Hybrid systems

Reference:
B. De Schutter and T.J.J. van den Boom, "Model predictive control for discrete-event and hybrid systems - Part II: Hybrid systems," Proceedings of the 16th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2004), Leuven, Belgium, 10 pp., July 2004. Paper 313.

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 and its companion paper ("Part I: Discrete-Event Systems") we give an overview of some results in connection with 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. After having discussed MPC for max-plus-linear discrete-event systems in the companion paper, we now discuss MPC for some classes of hybrid systems, viz. mixed logical dynamical systems, max-min-plus-scaling systems, and continuous piecewise-affine systems.