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Online identification of a two-mass system in frequency domain using a Kalman filter

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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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MLA
Nevaranta, Niko, et al. “Online Identification of a Two-Mass System in Frequency Domain Using a Kalman Filter.” MODELING IDENTIFICATION AND CONTROL, vol. 37, no. 2, Norwegian Society of Automatic Control, 2016, pp. 133–47, doi:10.4173/mic.2016.2.5.
APA
Nevaranta, N., Derammelaere, S., Parkkinen, J., Vervisch, B., Lindh, T., Niemela, M., & Pyrhonen, O. (2016). Online identification of a two-mass system in frequency domain using a Kalman filter. MODELING IDENTIFICATION AND CONTROL, 37(2), 133–147. https://doi.org/10.4173/mic.2016.2.5
Chicago author-date
Nevaranta, Niko, Stijn Derammelaere, Jukka Parkkinen, Bram Vervisch, Tuomo Lindh, Markku Niemela, and Olli Pyrhonen. 2016. “Online Identification of a Two-Mass System in Frequency Domain Using a Kalman Filter.” MODELING IDENTIFICATION AND CONTROL 37 (2): 133–47. https://doi.org/10.4173/mic.2016.2.5.
Chicago author-date (all authors)
Nevaranta, Niko, Stijn Derammelaere, Jukka Parkkinen, Bram Vervisch, Tuomo Lindh, Markku Niemela, and Olli Pyrhonen. 2016. “Online Identification of a Two-Mass System in Frequency Domain Using a Kalman Filter.” MODELING IDENTIFICATION AND CONTROL 37 (2): 133–147. doi:10.4173/mic.2016.2.5.
Vancouver
1.
Nevaranta N, Derammelaere S, Parkkinen J, Vervisch B, Lindh T, Niemela M, et al. Online identification of a two-mass system in frequency domain using a Kalman filter. MODELING IDENTIFICATION AND CONTROL. 2016;37(2):133–47.
IEEE
[1]
N. Nevaranta et al., “Online identification of a two-mass system in frequency domain using a Kalman filter,” MODELING IDENTIFICATION AND CONTROL, vol. 37, no. 2, pp. 133–147, 2016.
@article{8511362,
  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.}},
  author       = {{Nevaranta, Niko and Derammelaere, Stijn and Parkkinen, Jukka and Vervisch, Bram and Lindh, Tuomo and Niemela, Markku and Pyrhonen, Olli}},
  issn         = {{0332-7353}},
  journal      = {{MODELING IDENTIFICATION AND CONTROL}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{133--147}},
  publisher    = {{Norwegian Society of Automatic Control}},
  title        = {{Online identification of a two-mass system in frequency domain using a Kalman filter}},
  url          = {{http://doi.org/10.4173/mic.2016.2.5}},
  volume       = {{37}},
  year         = {{2016}},
}

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