Highly efficient parameter estimation algorithms for Hammerstein non-linear systems

Hammerstein system identification is difficult because there exist the product items of the parameters between the non-linear block and the linear block. This study presents a novel parameter separation based recursive least squares (PS-RLS) identification algorithm for resolving this problem. Its b...

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Veröffentlicht in:	IET control theory & applications Jg. 13; H. 4; S. 477 - 485
Hauptverfasser:	Mao, Yawen, Ding, Feng, Xu, Ling, Hayat, Tasawar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 05.03.2019
Schlagworte:	Hammerstein nonlinear systems Hammerstein system identification highly accurate parameter identification submodels least squares approximations linear block linear filter nonlinear block nonlinear control systems over‐parametrisation based recursive least squares method parameter estimation parameter estimation algorithms parameter separation parameter vector PS‐RLS algorithm recursive least squares identification algorithm Research Article least squares approximations identification submodels nonlinear block linear block over-parametrisation based recursive least squares method recursive least squares identification algorithm parameter estimation algorithms parameter separation Hammerstein system identification PS-RLS algorithm parameter vector linear filter Hammerstein nonlinear systems nonlinear control systems parameter estimation highly accurate parameter
ISSN:	1751-8644, 1751-8652
Online-Zugang:	Volltext
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