DocumentCode :
2466410
Title :
Fast model order selection of the nonlinearity in hammerstein systems
Author :
Mzyk, Grzegorz
Author_Institution :
Inst. of Comput. Eng., Control & Robot., Wroclaw Univ. of Technol., Wroclaw, Poland
fYear :
2012
fDate :
28-31 May 2012
Firstpage :
507
Lastpage :
510
Abstract :
In the paper we show a new three-stage algorithm identifying the Hammerstein system nonlinearity. The algorithm is designed to work when a poor a priori knowledge is available and when the measurement data set is small. In the first stage, a deconvolution routine is applied to output signal in order to diminish a (usually) harmful influence of the linear dynamic component. Such filtered output is then used in a standard nonparametric estimate (be it kernel or orthogonal series one) to recover the unknown characteristics. Finally, the results of nonparametric estimation are used in the algorithm selecting the best parametric model. The entire proposed approach is illustrated by the simulation example.
Keywords :
control nonlinearities; deconvolution; filtering theory; nonlinear control systems; nonparametric statistics; Hammerstein system nonlinearity; deconvolution routine; inverse filter; linear dynamic component; model order selection; nonparametric estimation; parametric model; Computational modeling; Deconvolution; Estimation; Kernel; Noise; Parametric statistics; Polynomials; Nonparametric identification; inverse filtering; kernel regression; orthogonal series expansion; structure detection Hammerstein system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Carpathian Control Conference (ICCC), 2012 13th International
Conference_Location :
High Tatras
Print_ISBN :
978-1-4577-1867-0
Type :
conf
DOI :
10.1109/CarpathianCC.2012.6228696
Filename :
6228696
Link To Document :
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