Towards Model Order Selection for Robust-Control-Relevant System Identification

P.J.M.M. Tacx; Robin de Rozario; Tom A.E. Oomen

Towards Model Order Selection for Robust-Control-Relevant System Identification

P.J.M.M. Tacx, Robin de Rozario, Tom A.E. Oomen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

Samenvatting

Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key observation is that model accuracy and model complexity should depend on the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems. Simulations confirm that the model order depends on the control objectives. Overall, the framework enables systematic identification of model sets for robust control.

Originele taal-2	Engels
Titel	Preprints 21st IFAC World Congress 2020
Aantal pagina's	1
Status	Gepubliceerd - 2020
Evenement	21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress) - Berlin, Duitsland Duur: 12 jul. 2020 → 17 jul. 2020 Congresnummer: 21 https://www.ifac2020.org/

Congres

Congres	21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress)
Verkorte titel	IFAC 2020
Land/Regio	Duitsland
Stad	Berlin
Periode	12/07/20 → 17/07/20
Internet adres	https://www.ifac2020.org/

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http://www.dct.tue.nl/toomen/files/TacxRozOom2020b.pdf

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@inproceedings{069acd07cc3b41e7b1fde94573d7fe5a,

title = "Towards Model Order Selection for Robust-Control-Relevant System Identification",

abstract = "Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key observation is that model accuracy and model complexity should depend on the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems. Simulations confirm that the model order depends on the control objectives. Overall, the framework enables systematic identification of model sets for robust control.",

keywords = "Identification for Control, Robust control, Motion control, Mechatronic systems, Frequency domain identification, Identification and control methods, Order selection",

author = "P.J.M.M. Tacx and {de Rozario}, Robin and Oomen, {Tom A.E.}",

year = "2020",

language = "English",

booktitle = "Preprints 21st IFAC World Congress 2020",

note = "21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress), IFAC 2020 ; Conference date: 12-07-2020 Through 17-07-2020",

url = "https://www.ifac2020.org/",

}

TY - GEN

T1 - Towards Model Order Selection for Robust-Control-Relevant System Identification

AU - Tacx, P.J.M.M.

AU - de Rozario, Robin

AU - Oomen, Tom A.E.

N1 - Conference code: 21

PY - 2020

Y1 - 2020

N2 - Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key observation is that model accuracy and model complexity should depend on the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems. Simulations confirm that the model order depends on the control objectives. Overall, the framework enables systematic identification of model sets for robust control.

AB - Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key observation is that model accuracy and model complexity should depend on the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems. Simulations confirm that the model order depends on the control objectives. Overall, the framework enables systematic identification of model sets for robust control.

KW - Identification for Control

KW - Robust control

KW - Motion control

KW - Mechatronic systems

KW - Frequency domain identification

KW - Identification and control methods

KW - Order selection

M3 - Conference contribution

BT - Preprints 21st IFAC World Congress 2020

T2 - 21st World Congress of the International Federation of Aufomatic Control (IFAC 2020 World Congress)

Y2 - 12 July 2020 through 17 July 2020

ER -

Towards Model Order Selection for Robust-Control-Relevant System Identification

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Congres

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