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.
In: Robust Control Design. Vol. 7 International Federation of Automatic Control, 2012. p. 616-621 (IFAC Proceedings Volumes (IFAC-PapersOnline) ).Publication: Research - peer-review › Article in proceedings – Annual report year: 2012
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RIS
TY - GEN
T1 - Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain
A1 - Mirzaei,Mahmood
A1 - Poulsen,Niels Kjølstad
A1 - Niemann,Hans Henrik
AU - Mirzaei,Mahmood
AU - Poulsen,Niels Kjølstad
AU - Niemann,Hans Henrik
PB - International Federation of Automatic Control
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
SN - 978-3-902823-03-8
VL - 7
BT - Robust Control Design
T2 - Robust Control Design
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
T3 - en_GB
SP - 616
EP - 621
ER -