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

Authors: Niko Nevaranta, Stijn Derammelaere, Jukka Parkkinen, Bram Vervisch, Tuomo Lindh, Markku Niemelä and Olli Pyrhönen,
Affiliation: Lappeenranta University of Technology and Ghent University
Reference: 2016, Vol 37, No 2, pp. 133-147.

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Keywords: Kalman filter, Non-parametric estimation, Online identification, Short-time DFT, Two-mass system

Abstract: 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.

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BibTeX:
@article{MIC-2016-2-5,
  title={{Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter}},
  author={Nevaranta, Niko and Derammelaere, Stijn and Parkkinen, Jukka and Vervisch, Bram and Lindh, Tuomo and Niemelä, Markku and Pyrhönen, Olli},
  journal={Modeling, Identification and Control},
  volume={37},
  number={2},
  pages={133--147},
  year={2016},
  doi={10.4173/mic.2016.2.5},
  publisher={Norwegian Society of Automatic Control}
};

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