A1 Journal article (refereed), original research

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Open Access publication

Publication Details
Authors: Nevaranta Niko, Derammelaere Stijn, Parkkinen Jukka, Vervisch Bram, Lindh Tuomo, Niemelä Markku, Pyrhönen Olli
Publication year: 2016
Language: English
Related Journal or Series Information: Modeling, Identification and Control
Volume number: 37
Issue number: 2
Start page: 133
End page: 147
JUFO-Level of this publication: 1
Open Access: Open Access publication

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies.

Research Areas

Last updated on 2018-19-10 at 08:49