Conference paper
MPC for uncertain systems using the Youla parameterizations
Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark1
Department of Informatics and Mathematical Modeling, Technical University of Denmark2
Automation, Department of Electrical Engineering, Technical University of Denmark3
Department of Electrical Engineering, Technical University of Denmark4
Several approaches have been taken in the past to deal with uncertainty in constrained predictive control. The major drawbacks of these efforts are usually either conservativeness and/or on-line computational complexity. In this work we examine the possibility of dealing with uncertainty through the use of the primary and the dual Youla parameterizations.
The dual Youla parameter can be seen as a frequency weighted measure of the uncertainty and the primary Youla parameter can be seen as a controller for this uncertainty. The work is an application of the methodology in [12] to constraint control.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2008 |
| Pages: | 3421-3426 |
| Proceedings: | 47th IEEE Conference on Decision and Control |
| ISBN: | 1424431239 , 1424431247 , 150907614X , 150907614x , 9781424431236 , 9781424431243 and 9781509076147 |
| ISSN: | 01912216 |
| Types: | Conference paper |
| DOI: | 10.1109/CDC.2008.4738799 |
| ORCIDs: | Niemann, Hans Henrik and Poulsen, Niels Kjølstad |
Computational complexity Control systems Cost function Frequency measurement Industrial control MPC Measurement uncertainty Predictive control Predictive models Weight measurement computational complexity constrained predictive control constraint control dual Youla parameter model predictive control predictive control uncertain systems
