Grey-box modeling of friction: an experimental case-study

R.H.A. Hensen; G.Z. Angelis; M.J.G. Van De Molengraft; Bram de Jager; J.J. Kok

Grey-box modeling of friction: an experimental case-study

R.H.A. Hensen, G.Z. Angelis, M.J.G. Van De Molengraft, Bram de Jager, J.J. Kok

Control Systems Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

266 Downloads (Pure)

Abstract

Grey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used iteratively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models.

Original language	English
Title of host publication	1999 European Control Conference, ECC 1999, 31 August - 3 September 1999, Karlsruhe, Germany
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	3148-3153
Number of pages	6
ISBN (Print)	978-3-9524173-5-5
Publication status	Published - 24 Mar 2015
Event	5th European Control Conference, ECC 1999 - Karlsruhe, Germany Duration: 31 Aug 1999 → 3 Sept 1999 Conference number: 5

Conference

Conference	5th European Control Conference, ECC 1999
Abbreviated title	ECC99
Country/Territory	Germany
City	Karlsruhe
Period	31/08/99 → 3/09/99

Keywords

extended Kalman filtering
Friction models
identification
neural networks
polytopic model

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title = "Grey-box modeling of friction: an experimental case-study",

abstract = "Grey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used iteratively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models.",

keywords = "extended Kalman filtering, Friction models, identification, neural networks, polytopic model",

author = "R.H.A. Hensen and G.Z. Angelis and \{Van De Molengraft\}, M.J.G. and \{de Jager\}, Bram and J.J. Kok",

year = "2015",

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Hensen, RHA, Angelis, GZ, Van De Molengraft, MJG, de Jager, B & Kok, JJ 2015, Grey-box modeling of friction: an experimental case-study. in 1999 European Control Conference, ECC 1999, 31 August - 3 September 1999, Karlsruhe, Germany., 7099811, Institute of Electrical and Electronics Engineers, Piscataway, pp. 3148-3153, 5th European Control Conference, ECC 1999, Karlsruhe, Germany, 31/08/99.

Grey-box modeling of friction: an experimental case-study. / Hensen, R.H.A.; Angelis, G.Z.; Van De Molengraft, M.J.G. et al.
1999 European Control Conference, ECC 1999, 31 August - 3 September 1999, Karlsruhe, Germany. Piscataway: Institute of Electrical and Electronics Engineers, 2015. p. 3148-3153 7099811.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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T1 - Grey-box modeling of friction

T2 - 5th European Control Conference, ECC 1999

AU - Hensen, R.H.A.

AU - Angelis, G.Z.

AU - Van De Molengraft, M.J.G.

AU - de Jager, Bram

AU - Kok, J.J.

N1 - Conference code: 5

PY - 2015/3/24

Y1 - 2015/3/24

N2 - Grey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used iteratively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models.

AB - Grey-box modeling covers the domain where we want to use a balanced amount of first principles and empiricism. The two generic grey-box models presented, i.e., a Neural Network model and a Polytopic model are capable of identifying friction characteristics that are left unexplained by first principles modeling. In an experimental case study, both grey-box models are applied to identify a rotating arm subjected to friction. An augmented state extended Kalman filter is used iteratively and off-line for the estimation of unknown parameters. For the studied example and defined black-box topologies, little difference is observed between the two models.

KW - extended Kalman filtering

KW - Friction models

KW - identification

KW - neural networks

KW - polytopic model

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M3 - Conference contribution

AN - SCOPUS:84930608641

SN - 978-3-9524173-5-5

SP - 3148

EP - 3153

BT - 1999 European Control Conference, ECC 1999, 31 August - 3 September 1999, Karlsruhe, Germany

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

Y2 - 31 August 1999 through 3 September 1999

ER -

Grey-box modeling of friction: an experimental case-study

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Keywords

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