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Nonlinear Approaches to Periodic Signal Modeling
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2005 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Periodic signal modeling plays an important role in different fields. The unifying theme of this thesis is using nonlinear techniques to model periodic signals. The suggested techniques utilize the user pre-knowledge about the signal waveform. This gives these techniques an advantage as compared to others that do not consider such priors.

The technique of Part I relies on the fact that a sine wave that is passed through a static nonlinear function produces a harmonic spectrum of overtones. Consequently, the estimated signal model can be parameterized as a known periodic function (with unknown frequency) in cascade with an unknown static nonlinearity. The unknown frequency and the parameters of the static nonlinearity are estimated simultaneously using the recursive prediction error method (RPEM). A treatment of the local convergence properties of the RPEM is provided. Also, an adaptive grid point algorithm is introduced to estimate the unknown frequency and the parameters of the static nonlinearity in a number of adaptively estimated grid points. This gives the RPEM more freedom to select the grid points and hence reduces modeling errors.

Limit cycle oscillations problem are encountered in many applications. Therefore, mathematical modeling of limit cycles becomes an essential topic that helps to better understand and/or to avoid limit cycle oscillations in different fields. In Part II, a second-order nonlinear ODE is used to model the periodic signal as a limit cycle oscillation. The right hand side of the ODE model is parameterized using a polynomial function in the states, and then discretized to allow for the implementation of different identification algorithms. Hence, it is possible to obtain highly accurate models by only estimating a few parameters.

In Part III, different user aspects for the two nonlinear approaches of the thesis are discussed. Finally, topics for future research are presented.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2005. , p. 231
Series
Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-2516 ; 59
Keywords [en]
Cramer-Rao bounds, frequency estimation, identification, limit cycle, nonlinear systems, periodic signals, Wiener model structure
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:uu:diva-4644ISBN: 91-554-6079-8 (print)OAI: oai:DiVA.org:uu-4644DiVA, id: diva2:165360
Public defence
2005-01-26, Room 2247, MIC 2, MIC, Uppsala, 10:15
Opponent
Supervisors
Available from: 2004-12-29 Created: 2004-12-29 Last updated: 2018-08-17Bibliographically approved

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Citation style
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