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Applications of Kautz Models in System Identification
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Royal Institute of Technology, Sweden.
1993 (English)In: Proceedings of the 12th IFAC World Congress, 1993, Vol. 5, 309-312 p.Conference paper (Refereed)
Abstract [en]

FIR, ARX or AR model structures can be used to describe many industrial processes. Simple linear regression techniques can be applied to estimate such models from experimental data. However, for low signal to noise ratios in combination with transfer function poles and noise model zeros close to the unit circle, a large number of model parameters are needed to generate adequate models. The Kautz model structure generalizes FIR, ARX and AR models. By using a priori knowledge about the dominating time constants and damping factors of the system, the model complexity is reduced, and the linear regression structure is retained. The objective of this contribution is to study an industrial example, where Kautz models have distinct advantages. The data investigated corresponds to aircraft flight flutter, which is a state when an aircraft component starts to oscillate.

Place, publisher, year, edition, pages
1993. Vol. 5, 309-312 p.
Keyword [en]
Modeling, System identification, Parameter estimation, Aircraft modeling, Kautz functions
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-94090ISBN: 978-0080422121OAI: diva2:629723
12th IFAC World Congress, Sydney, Australia, July, 1993
Available from: 2013-06-17 Created: 2013-06-16 Last updated: 2013-06-17

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