Reference
B. De Schutter and T. van den Boom, "Model predictive control for
max-plus-linear discrete event systems,"
Automatica,
vol. 37, no. 7, pp. 1049-1056, July 2001.
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 discrete-time
models. In this paper we extend MPC to a class of discrete-event systems that
can be described by models that are "linear" in the max-plus algebra, which has
maximization and addition as basic operations. In general the resulting
optimization problem are nonlinear and nonconvex. However, if the control
objective and the constraints depend monotonically on the outputs of the
system, the model predictive control problem can be recast as problem with a
convex feasible set. If in addition the objective function is convex, this
leads to a convex optimization problem, which can be solved very efficiently.
Publisher page
Downloads
Extended version
- B. De Schutter and T. van den Boom, "Model predictive control for max-plus-linear discrete-event systems: Extended report & addendum," Tech. report bds:99-10a, Control Systems Engineering, Fac. of Information Technology and Systems, Delft University of Technology, Delft, The Netherlands, 27 pp., Nov. 2000. A short version of this report has been published in Automatica, vol. 37, no. 7, pp. 1049-1056, July 2001. (abstract, bibtex, report (pdf))
BibTeX
@article{DeSvan:99-10,
author = {De Schutter, Bart and van den Boom, Ton},
title = {Model Predictive Control for Max-Plus-Linear Discrete Event
Systems},
journal = {Automatica},
volume = {37},
number = {7},
pages = {1049--1056},
month = jul,
year = {2001}
}