Conference paper
Robust stability in predictive control with soft constraints
Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark1
Department of Informatics and Mathematical Modeling, Technical University of Denmark2
Automation and Control, Department of Electrical Engineering, Technical University of Denmark3
Department of Electrical Engineering, Technical University of Denmark4
In this paper we take advantage of the primary and dual Youla parameterizations for setting up a soft constrained model predictive control (MPC) scheme for which stability is guaranteed in face of norm-bounded uncertainties. Under special conditions guarantees are also given for hard input constraints.
In more detail, we parameterize the MPC predictions in terms of the primary Youla parameter and use this parameter as the online optimization variable. The uncertainty is parameterized in terms of the dual Youla parameter. Stability can then be guaranteed through small gain arguments on the loop consisting of the primary and dual Youla parameter.
This is included in the MPC optimization as a constraint on the induced gain of the optimization variable. We illustrate the method with a numerical simulation example.
Language: | English |
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Publisher: | IEEE |
Year: | 2010 |
Pages: | 6280-6285 |
Proceedings: | American Control Conference (ACC 2010) |
ISBN: | 1424474256 , 1424474264 , 1424474272 , 9781424474257 , 9781424474264 and 9781424474271 |
ISSN: | 23785861 and 07431619 |
Types: | Conference paper |
DOI: | 10.1109/ACC.2010.5531514 |
ORCIDs: | Niemann, Hans Henrik and Poulsen, Niels Kjølstad |