Model predictive control for railway networks
- 13 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 105-110
- https://doi.org/10.1109/aim.2001.936438
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.Keywords
This publication has 5 references indexed in Scilit:
- Efficient Solution of Dynamic Optimization and NMPC ProblemsPublished by Springer Nature ,2000
- The extended linear complementarity problemMathematical Programming, 1995
- Generating optimal schedules for an underground railway linePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1995
- Model predictive control: Theory and practice—A surveyAutomatica, 1989
- Generalized predictive control—Part I. The basic algorithmAutomatica, 1987