Identification of Wiener Models in the Presence of Structural Disturbances

Amir Shirdel, Jari Böling, Hannu Toivonen

Forskningsoutput: Kapitel i bok/konferenshandlingKonferensbidragVetenskapligPeer review

1 Citeringar (Scopus)

Sammanfattning

In empirical system identification, non-stationary structural disturbances, such as trends and outliers, can have a negative effect on the estimation of the system parameters. As it not possible to determine a priori which parts of the measured data stem from structural disturbances and which are due to the system dynamics, the identification of structural disturbances and system model should be done simultaneously. In this study, a method for output error identification of nonlinear Wiener models in the case when the measurement is affected by trends and outliers is presented. The Wiener model can be described by a dynamic linear block followed by an static nonlinear block. In the proposed method the dynamic block is expanded using orthonormal basis functions, while the static nonlinear block is modeled by a kernel model. The kernel parameters and structural disturbances are estimated simultaneously by using sparse optimization, which is solved using l1-regularization and iterative reweighting. The feasibility of the proposed method is demonstrated on a simulated example.

OriginalspråkOdefinierat/okänt
Titel på gästpublikation20th IFAC World Congress
RedaktörerDenis Dochain, Didier Henrion, Dimitri Peaucelle
Sidor14094–14099
DOI
StatusPublicerad - 2017
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangIFAC World Congress - 20th IFAC World Congress
Varaktighet: 9 jul 201714 jul 2017

Konferens

KonferensIFAC World Congress
Period09/07/1714/07/17

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