Neural network based active vibration absorber with output feedback control

R. P. Ma, Alok Sinha

    Research output: Contribution to journalArticle

    Abstract

    A neural-network-based output feedback control system has been presented to suppress vibration in a flexible structure for a wide range of excitation frequencies. The control theory developed by Johnson (1971, 1972) is used; and a multi-layer neural network (MNN) is used to learn the mapping between the excitation frequency and optimal control gains. Numerical results are presented for a single-degree-of-freedom spring-mass system to illustrate the effectiveness of this neural network based output feedback vibration absorber.

    Original languageEnglish (US)
    Pages (from-to)1023-1028
    Number of pages6
    JournalArtificial Neural Networks in Engineering - Proceedings (ANNIE'94)
    Volume4
    StatePublished - 1994

    Fingerprint

    Feedback control
    Neural networks
    Flexible structures
    Gain control
    Multilayer neural networks
    Control theory
    Feedback
    Control systems

    All Science Journal Classification (ASJC) codes

    • Engineering(all)

    Cite this

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    title = "Neural network based active vibration absorber with output feedback control",
    abstract = "A neural-network-based output feedback control system has been presented to suppress vibration in a flexible structure for a wide range of excitation frequencies. The control theory developed by Johnson (1971, 1972) is used; and a multi-layer neural network (MNN) is used to learn the mapping between the excitation frequency and optimal control gains. Numerical results are presented for a single-degree-of-freedom spring-mass system to illustrate the effectiveness of this neural network based output feedback vibration absorber.",
    author = "Ma, {R. P.} and Alok Sinha",
    year = "1994",
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    AU - Sinha, Alok

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    AB - A neural-network-based output feedback control system has been presented to suppress vibration in a flexible structure for a wide range of excitation frequencies. The control theory developed by Johnson (1971, 1972) is used; and a multi-layer neural network (MNN) is used to learn the mapping between the excitation frequency and optimal control gains. Numerical results are presented for a single-degree-of-freedom spring-mass system to illustrate the effectiveness of this neural network based output feedback vibration absorber.

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