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

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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 languageEnglish
Title of host publication1999 European Control Conference, ECC 1999, 31 August - 3 September 1999, Karlsruhe, Germany
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3148-3153
Number of pages6
ISBN (Print)978-3-9524173-5-5
Publication statusPublished - 24 Mar 2015
Event5th European Control Conference (ECC99) - Karlsruhe, Germany
Duration: 31 Aug 19993 Sep 1999
Conference number: 5

Conference

Conference5th European Control Conference (ECC99)
Abbreviated titleECC99
CountryGermany
CityKarlsruhe
Period31/08/993/09/99

Keywords

  • extended Kalman filtering
  • Friction models
  • identification
  • neural networks
  • polytopic model

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