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

Mahmood Mirzaei; Niels Kjølstad Poulsen; Hans Henrik Niemann

doi:10.3182/20120620-3-DK-2025.00107

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

Mahmood Mirzaei, Niels Kjølstad Poulsen, Hans Henrik Niemann

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

561 Downloads (Pure)

Abstract

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 language	English
Title of host publication	Robust Control Design
Volume	7
Publisher	International Federation of Automatic Control
Publication date	2012
Pages	616-621
ISBN (Print)	978-3-902823-03-8
DOIs	https://doi.org/10.3182/20120620-3-DK-2025.00107
Publication status	Published - 2012
Event	7th IFAC Symposium on Robust Control Design (ROCOND) - Aalborg Congress & Culture Centre, Aalborg, Denmark Duration: 20 Jun 2012 → 22 Jun 2012 http://www.rocond12.org/

Conference

Conference	7th IFAC Symposium on Robust Control Design (ROCOND)
Location	Aalborg Congress & Culture Centre
Country/Territory	Denmark
City	Aalborg
Period	20/06/2012 → 22/06/2012
Internet address	http://www.rocond12.org/

Series	IFAC Proceedings Volumes (IFAC-PapersOnline)

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

Access to Document

10.3182/20120620-3-DK-2025.00107

Rmpc lpv ifac

OpenUrl availability

Full text

Cite this

@inproceedings{d42e431af10d4de58f2a11ef889025ff,

title = "Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain",

abstract = "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.",

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",

author = "Mahmood Mirzaei and Poulsen, {Niels Kj{\o}lstad} and Niemann, {Hans Henrik}",

year = "2012",

doi = "10.3182/20120620-3-DK-2025.00107",

language = "English",

isbn = "978-3-902823-03-8",

volume = "7",

series = "IFAC Proceedings Volumes (IFAC-PapersOnline) ",

publisher = "International Federation of Automatic Control",

pages = "616--621",

booktitle = "Robust Control Design",

note = "7th IFAC Symposium on Robust Control Design (ROCOND) ; Conference date: 20-06-2012 Through 22-06-2012",

url = "http://www.rocond12.org/",

}

Mirzaei, M, Poulsen, NK & Niemann, HH 2012, Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain. in Robust Control Design. vol. 7, International Federation of Automatic Control, IFAC Proceedings Volumes (IFAC-PapersOnline) , pp. 616-621, 7th IFAC Symposium on Robust Control Design (ROCOND), Aalborg, Denmark, 20/06/2012. https://doi.org/10.3182/20120620-3-DK-2025.00107

Robust Model Predictive Control of a Nonlinear System with Known Scheduling Variable and Uncertain Gain. / Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik.
Robust Control Design. Vol. 7 International Federation of Automatic Control, 2012. p. 616-621 (IFAC Proceedings Volumes (IFAC-PapersOnline) ).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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

T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)

SP - 616

EP - 621

BT - Robust Control Design

PB - International Federation of Automatic Control

T2 - 7th IFAC Symposium on Robust Control Design (ROCOND)

Y2 - 20 June 2012 through 22 June 2012

ER -

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

Abstract

Conference

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this