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

    7 Citations (Scopus)
    10 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.",
    keywords = "Active vibration isolation, High-precision mechatronics, Least mean squares optimization, MIMO feedforward control",
    author = "M.A. Beijen and M.F. Heertjes and {Van Dijk}, J. and Hakvoort, {W. B.J.}",
    year = "2018",
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    language = "English",
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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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    T1 - Self-tuning MIMO disturbance feedforward control for active hard-mounted vibration isolators

    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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    KW - High-precision mechatronics

    KW - Least mean squares optimization

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