Reference:
B. De Schutter and
T. van den Boom,
"Model predictive control for max-plus-linear discrete-event systems:
Extended report & Addendum," Tech. rep. 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:
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 report 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 non-convex. 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.
Technical report:
pdf
file
(282 KB)