Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

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Robust model predictive control (RMPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Because of the special structure of the problem, uncertainty is only in the B matrix (gain) of the state space model. Therefore by taking advantage of this structure, we formulate a tractable minimax optimization problem to solve robust model predictive control problem. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon.
Original languageEnglish
TitleRobust Control Design
Volume7
PublisherInternational Federation of Automatic Control
Publication date2012
Pages616-621
ISBN (print)978-3-902823-03-8
DOIs
StatePublished

Conference

Conference7th IFAC Symposium on Robust Control Design (ROCOND)
CountryDenmark
CityAalborg
Period20/06/1222/06/12
Internet addresshttp://www.rocond12.org/
NameIFAC Proceedings Volumes (IFAC-PapersOnline)
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Control, Nonlinear systems, Optical radar, Scheduling, State space methods, Wind effects, Wind turbines, Robust control, LIDAR measurements, Linear parameter varying, Robust model predictive control, Lidar measurements, Linear parameter varying models, Minimax optimization, Prediction horizon, Scheduling variable, Special structure, State prediction, State space model, Wind speed
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