Parametric Versus Nonparametric Approach to Wiener Systems Identification

Mzyk, Grzegorz

doi:10.1007/978-1-84996-513-2_8

Cited by 9 publications

(1 citation statement)

References 42 publications

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“…The available identification methods for nonparametric Wiener systems include stochastic methods (e.g. Greblicki and Pawlak, 2008;Mzyk, 2010) and frequency methods (e.g. Schoukens, 2001, 2005;Bai, 2003;Giri et al, 2009;Schoukens and Rolain, 2012).…”

Section: Introductionmentioning

confidence: 99%

System identification of a class of Wiener systems with hysteretic nonlinearities

Radouane

Giri

Ikhouane

et al. 2016

Adaptive Control & Signal

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Existing works on Wiener system identification have essentially been focused on the case where the output nonlinearity is memoryless. When memory nonlinearities have been considered, the focus has been restricted to backlash like nonlinearities. In this paper, we are considering Wiener systems where the output nonlinearity is a general hysteresis operator captured by the well-known Bouc-Wen model. The Wiener system identification problem is addressed by making use of a steady-state property, obtained in periodic regime, referred to as hysteretic loop assumption'. The complexity of this problem comes from the system nonlinearity as well as its unknown parameters that enter in a non-affine way in the model. It is shown that the linear part of the system is accurately identified using a frequency method. Then, the nonlinear hysteretic subsystem is identified, on the basis of a parameterized representation, using a prediction-error approach.Postprint (author's final draft

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