Model predictive control for railway networks

Abstract

Model predictive control (MPC) is a very popular controller design method in the process industry. MPC often uses linear discrete-time models. In this paper we extend MPC to a class of discrete-event systems with both hard and soft synchronization constraints. Typical examples of such systems are railway networks, subway networks, and other logistic operations. In general the MPC control design problem for these systems leads to a nonlinear non-convex optimization problem. We also show that the optimal MPC strategy can be computed using an extended linear complementarity problem.