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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Vydané v:	IET control theory & applications Ročník 13; číslo 4; s. 477 - 485
Hlavní autori:	Mao, Yawen, Ding, Feng, Xu, Ling, Hayat, Tasawar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 05.03.2019
Predmet:	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
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Shrnutí:	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 basic idea is to use a linear filter to filter the output data and the noise, and then to obtain two new identification submodels in each of which the output is linear in the corresponding parameter vector. Compared with the over-parametrisation based recursive least squares method, the proposed algorithm can avoid estimating the redundant parameters and has a higher computational efficiency. The simulation results show that the proposed PS-RLS algorithm can generate highly accurate parameter estimates with less computational effort.
ISSN:	1751-8644 1751-8652
DOI:	10.1049/iet-cta.2018.5411