Reference
I. Necoara, B. De Schutter, T. van den Boom, and H. Hellendoorn, "Model
predictive control for uncertain max-min-plus-scaling systems,"
International Journal of Control, vol. 81, no. 5, pp.
701-713, May 2008.
Abstract
In this paper we extend the classical min-max model predictive control
framework to a class of uncertain discrete event systems that can be modeled
using the operations maximization, minimization, addition and scalar
multiplication, and that we call max-min-plus-scaling (MMPS) systems. Provided
that the stage cost is an MMPS expression and considering only linear input
constraints then the open-loop min-max model predictive control problem for
MMPS systems can be transformed into a sequence of linear programming problems.
Hence, the min-max model predictive control problem for MMPS systems can be
solved efficiently, despite the fact that the system is nonlinear. A min-max
feedback model predictive control approach using disturbance feedback policies
is also presented, which leads to improved performance compared to the
open-loop approach.
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BibTeX
@article{NecDeS:06-035,
author = {Necoara, Ion and De Schutter, Bart and van den Boom, Ton and
Hellendoorn, Hans},
title = {Model Predictive Control for Uncertain Max-Min-Plus-Scaling
Systems},
journal = {International Journal of Control},
volume = {81},
number = {5},
pages = {701--713},
month = may,
year = {2008}
}