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...

Full description

Saved in:
Bibliographic Details
Published in:IET control theory & applications Vol. 13; no. 4; pp. 477 - 485
Main Authors: Mao, Yawen, Ding, Feng, Xu, Ling, Hayat, Tasawar
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 05.03.2019
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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