Filtered-error recursive least squares optimization for disturbance feedforward control in active vibration isolation

M.A. Beijen*, Wouter Bernardus Johannes Hakvoort

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

Abstract

This paper addresses the filtered-error recursive least squares (FeRLS) algorithm for disturbance feedforward control in active vibration isolation systems. The controller structure consists of a generalized finite-impulse response (FIR) filter to include a set of pre-determined poles and self-tuning zeros. In addition, residual noise shaping is included to add frequency weighting and improve robustness. Compared to existing filtered-error least mean squares (FeLMS) algorithms, two major improvements are distinguished. First, faster convergence is obtained without the necessity of pre-whitening and an orthonormal basis. Second, the parameters are estimated without steady-state variance. These improvements are demonstrated using simulation studies, which show the potential of the algorithm in active vibration isolators.
Original languageEnglish
Pages (from-to)448-453
Number of pages6
JournalIFAC-papersonline
Volume52
Issue number15
DOIs
Publication statusPublished - 20 Dec 2019
Event8th IFAC Symposium on Mechatronic Systems, MECHATRONICS 2019 - Technical University of Vienna, Vienna, Austria
Duration: 4 Sep 20196 Sep 2019
Conference number: 8
http://www.mechatronicsnolcos2019.org/

Fingerprint

Feedforward control
FIR filters
Poles
Tuning
Controllers

Keywords

  • Active vibration isolation
  • Disturbance feedforward control
  • Recursive least squares

Cite this

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abstract = "This paper addresses the filtered-error recursive least squares (FeRLS) algorithm for disturbance feedforward control in active vibration isolation systems. The controller structure consists of a generalized finite-impulse response (FIR) filter to include a set of pre-determined poles and self-tuning zeros. In addition, residual noise shaping is included to add frequency weighting and improve robustness. Compared to existing filtered-error least mean squares (FeLMS) algorithms, two major improvements are distinguished. First, faster convergence is obtained without the necessity of pre-whitening and an orthonormal basis. Second, the parameters are estimated without steady-state variance. These improvements are demonstrated using simulation studies, which show the potential of the algorithm in active vibration isolators.",
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Filtered-error recursive least squares optimization for disturbance feedforward control in active vibration isolation. / Beijen, M.A.; Hakvoort, Wouter Bernardus Johannes.

In: IFAC-papersonline, Vol. 52, No. 15, 20.12.2019, p. 448-453.

Research output: Contribution to journalConference articleAcademicpeer-review

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AU - Hakvoort, Wouter Bernardus Johannes

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N2 - This paper addresses the filtered-error recursive least squares (FeRLS) algorithm for disturbance feedforward control in active vibration isolation systems. The controller structure consists of a generalized finite-impulse response (FIR) filter to include a set of pre-determined poles and self-tuning zeros. In addition, residual noise shaping is included to add frequency weighting and improve robustness. Compared to existing filtered-error least mean squares (FeLMS) algorithms, two major improvements are distinguished. First, faster convergence is obtained without the necessity of pre-whitening and an orthonormal basis. Second, the parameters are estimated without steady-state variance. These improvements are demonstrated using simulation studies, which show the potential of the algorithm in active vibration isolators.

AB - This paper addresses the filtered-error recursive least squares (FeRLS) algorithm for disturbance feedforward control in active vibration isolation systems. The controller structure consists of a generalized finite-impulse response (FIR) filter to include a set of pre-determined poles and self-tuning zeros. In addition, residual noise shaping is included to add frequency weighting and improve robustness. Compared to existing filtered-error least mean squares (FeLMS) algorithms, two major improvements are distinguished. First, faster convergence is obtained without the necessity of pre-whitening and an orthonormal basis. Second, the parameters are estimated without steady-state variance. These improvements are demonstrated using simulation studies, which show the potential of the algorithm in active vibration isolators.

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