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Communication Dans Un Congrès Année : 2012

Nonparametric identification of LPV models under general noise conditions: an LS-SVM based approach

Résumé

Parametric identification approaches in the Linear Parameter-Varying (LPV) setting require optimal prior selection of a set of functional dependencies, used in the parametrization of the model coefficients, to provide accurate model estimates of the underlying system. Consequently, data-driven estimation of these functional dependencies has a paramount importance, especially when very limited a priori knowledge is available. Existing over- parametrization and nonparametric methods dedicated to nonlinear estimation offer interesting starting points for this problem, but need reformulation to be applied in the LPV setting. Moreover, most of these approaches are developed under quite restrictive auto-regressive noise assumptions. In this paper, a nonparametric Least-Squares Support Vector Machine (LS-SVM) approach is extended for the identification of LPV polynomial models. The efficiency of the approach in the considered noise setting is shown, but the drawback of the auto-regressive noise assumption is also demonstrated by a challenging LPV identification example. To preserve the attractive properties of the approach, but to overcome the drawbacks in the estimation of polynomial LPV models in a general noise setting, a recently developed Instrumental Variable (IV)-based extension of the LS-SVM method is applied. The performance of the introduced IV and the original LS-SVM approaches are compared in an identification study of an LPV system with unknown noise dynamics.
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Dates et versions

hal-00751720 , version 1 (11-12-2012)

Identifiants

Citer

Vincent Laurain, Roland Toth, Wei Xing Zheng, Marion Gilson. Nonparametric identification of LPV models under general noise conditions: an LS-SVM based approach. 16th IFAC Symposium on System Identification, SYSID'2012, Jul 2012, Bruxelles, Belgium. pp.1761-1766, ⟨10.3182/20120711-3-BE-2027.00230⟩. ⟨hal-00751720⟩
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