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

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

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**Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain.** / Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik.

Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

### Harvard

*Robust Control Design.*vol. 7, International Federation of Automatic Control, pp. 616-621. IFAC Proceedings Volumes (IFAC-PapersOnline) , , 10.3182/20120620-3-DK-2025.00107

### APA

*Robust Control Design.*(Vol. 7, pp. 616-621). International Federation of Automatic Control. (IFAC Proceedings Volumes (IFAC-PapersOnline) ). 10.3182/20120620-3-DK-2025.00107

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### MLA

*Robust Control Design.*International Federation of Automatic Control. 2012. 616-621. (IFAC Proceedings Volumes (IFAC-PapersOnline) ). Available: 10.3182/20120620-3-DK-2025.00107

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### Bibtex

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### RIS

TY - GEN

T1 - Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain

AU - Mirzaei,Mahmood

AU - Poulsen,Niels Kjølstad

AU - Niemann,Hans Henrik

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Control

KW - Nonlinear systems

KW - Optical radar

KW - Scheduling

KW - State space methods

KW - Wind effects

KW - Wind turbines

KW - Robust control

KW - LIDAR measurements

KW - Linear parameter varying

KW - Robust model predictive control

KW - Lidar measurements

KW - Linear parameter varying models

KW - Minimax optimization

KW - Prediction horizon

KW - Scheduling variable

KW - Special structure

KW - State prediction

KW - State space model

KW - Wind speed

U2 - 10.3182/20120620-3-DK-2025.00107

DO - 10.3182/20120620-3-DK-2025.00107

M3 - Article in proceedings

SN - 978-3-902823-03-8

VL - 7

SP - 616

EP - 621

BT - Robust Control Design

T2 - Robust Control Design

PB - International Federation of Automatic Control

ER -