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 |
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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 |
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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