Self-tuning MIMO disturbance feedforward control for active hard-mounted vibration isolators

M.A. Beijen (Corresponding Author), M.F. Heertjes, J. Van Dijk, W. B.J. Hakvoort

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)
6 Downloads (Pure)

Abstract

This paper proposes a multi-input multi-output (MIMO) disturbance feedforward controller to improve the rejection of floor vibrations in active vibration isolation systems for high-precision machinery. To minimize loss of performance due to model uncertainties, the feedforward controller is implemented as a self-tuning generalized FIR filter. This filter contains a priori knowledge of the poles, such that relatively few parameters have to be estimated which makes the algorithm computationally efficient. The zeros of the filter are estimated using the filtered-error least mean squares (FeLMS) algorithm. Residual noise shaping is used to reduce bias. Conditions on convergence speed, stability, bias, and the effects of sensor noise on the self-tuning algorithm are discussed in detail. The combined control strategy is validated on a 6-DOF Stewart platform, which serves as a multi-axis and hard-mounted vibration isolation system, and shows significant improvement in the rejection of floor vibrations.

Original languageEnglish
Pages (from-to)90-103
Number of pages14
JournalControl engineering practice
Volume72
DOIs
Publication statusPublished - 1 Mar 2018

Fingerprint

Vibration Isolation
Feedforward Control
Self-tuning
Feedforward control
Feedforward
Rejection
Tuning
Vibration
Disturbance
Stewart Platform
Filter
Controller
FIR Filter
Least Mean Square
Output
Convergence Speed
Model Uncertainty
Pole
Control Strategy
Controllers

Keywords

  • Active vibration isolation
  • High-precision mechatronics
  • Least mean squares optimization
  • MIMO feedforward control

Cite this

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title = "Self-tuning MIMO disturbance feedforward control for active hard-mounted vibration isolators",
abstract = "This paper proposes a multi-input multi-output (MIMO) disturbance feedforward controller to improve the rejection of floor vibrations in active vibration isolation systems for high-precision machinery. To minimize loss of performance due to model uncertainties, the feedforward controller is implemented as a self-tuning generalized FIR filter. This filter contains a priori knowledge of the poles, such that relatively few parameters have to be estimated which makes the algorithm computationally efficient. The zeros of the filter are estimated using the filtered-error least mean squares (FeLMS) algorithm. Residual noise shaping is used to reduce bias. Conditions on convergence speed, stability, bias, and the effects of sensor noise on the self-tuning algorithm are discussed in detail. The combined control strategy is validated on a 6-DOF Stewart platform, which serves as a multi-axis and hard-mounted vibration isolation system, and shows significant improvement in the rejection of floor vibrations.",
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Self-tuning MIMO disturbance feedforward control for active hard-mounted vibration isolators. / Beijen, M.A. (Corresponding Author); Heertjes, M.F.; Van Dijk, J.; Hakvoort, W. B.J.

In: Control engineering practice, Vol. 72, 01.03.2018, p. 90-103.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Beijen, M.A.

AU - Heertjes, M.F.

AU - Van Dijk, J.

AU - Hakvoort, W. B.J.

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AB - This paper proposes a multi-input multi-output (MIMO) disturbance feedforward controller to improve the rejection of floor vibrations in active vibration isolation systems for high-precision machinery. To minimize loss of performance due to model uncertainties, the feedforward controller is implemented as a self-tuning generalized FIR filter. This filter contains a priori knowledge of the poles, such that relatively few parameters have to be estimated which makes the algorithm computationally efficient. The zeros of the filter are estimated using the filtered-error least mean squares (FeLMS) algorithm. Residual noise shaping is used to reduce bias. Conditions on convergence speed, stability, bias, and the effects of sensor noise on the self-tuning algorithm are discussed in detail. The combined control strategy is validated on a 6-DOF Stewart platform, which serves as a multi-axis and hard-mounted vibration isolation system, and shows significant improvement in the rejection of floor vibrations.

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