14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeB1.3

Toth, Roland (Delft Univ. of Tech.), Heuberger, Peter S.C. (Delft Univ. of Tech.), Van den Hof, Paul M.J. (Delft Univ. of Tech.)

Optimal Pole Selection for LPV System Identification with OBFs, a Clustering Approach

Scheduled for presentation during the Regular Session "Identification of Hybrid and Parameter Varying Systems" (WeB1), Wednesday, March 29, 2006, 16:10−16:30, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 17, 2018

Keywords Nonlinear System Identification, Basis Functions


A fuzzy clustering approach is studied for optimal pole selection of Orthonormal Basis Functions (OBFs) used for the identification of Linear Parameter Varying (LPV) systems. The identification approach is based on interpolation of locally identified Linear Time Invariant (LTI) models, using globally fixed OBFs. The selection of the optimal OBF structure, that guarantees the least worst- case local modelling error, is accomplished through the joint application of the Kolmogorov n-width theory and Fuzzy c-Means (FCM) clustering of observed sample system poles. For the problem at hand, FCM solutions are given, based on three different metrics, and the qualities of the results are compared in terms of the derived OBFs.