A4 Conference proceedings

Recursive Parameter Estimation of a Mechanical System in Frequency Domain

Publication Details
Authors: Nevaranta Niko, Montonen Jan-Henri, Lindh Tuomo, Niemelä Markku, Pyrhönen Olli
Publication year: 2017
Language: English
Title of parent publication: 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)
Start page: 1
End page: 7
Number of pages: 7
ISBN: 978-1-5090-0410-2
eISBN: 978-1-5090-0409-6
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


Frequency-domain identification and parameter estimation
methods are well established and commonly applied
for commissioning and diagnostics purposes in electric drives. In
this paper, the feasibility of a recursive least squares parameter
estimation algorithm from frequency-domain observations is
studied. The identification problem is treated from two different
perspectives: first, by estimating a discrete autoregressive
model with exogenous terms (ARX) from the discrete Fourier
transforms (DFTs) of the input-output signals obtained from
the identification experiment and second, a nonparametric model
that is fitted in terms of least squares regression. Both proposed
identification approaches are studied by simulations and experimentally
validated by a closed-loop-controlled servomechanism

Last updated on 2018-19-10 at 07:55