A two-experiment approach to Wiener system identification. (July 2018)

• Record Type: Journal Article
• Authors: Bottegal, Giulio; Castro-Garcia, Ricardo; Suykens, Johan A.K.
• Is Part Of: Automatica. Volume 93(2018)
• Page Start: 282
• Page End: 289
• Publication Date: 2018-07
• Subjects: System identification -- Wiener systems -- Experiment design -- Least squares support vector machines; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2018.03.069
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: We propose a new methodology for identifying Wiener systems using the data acquired from two separate experiments. In the first experiment, we feed the system with a sinusoid at a prescribed frequency and use the steady state response of the system to estimate the static nonlinearity. In the second experiment, the estimated nonlinearity is used to identify a model of the linear block, feeding the system with a persistently exciting input. We discuss both parametric and nonparametric approaches to estimate the static nonlinearity. In the parametric case, we show that modeling the static nonlinearity as a polynomial results into a fast least-squares based estimation procedure. In the nonparametric case, least squares support vector machines (LS-SVM) are employed to obtain a flexible model. The effectiveness of the method is demonstrated through numerical experiments.