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    Neural Network Emulation of Inverse Dynamics for a Magnetorheological Damper

    Source: Journal of Structural Engineering:;2002:;Volume ( 128 ):;issue: 002
    Author:
    Chih-Chen Chang
    ,
    Li Zhou
    DOI: 10.1061/(ASCE)0733-9445(2002)128:2(231)
    Publisher: American Society of Civil Engineers
    Abstract: The dynamic behavior of a magnetorheological (MR) damper is well portrayed using a Bouc–Wen hysteresis model. This model estimates damper forces based on the inputs of displacement, velocity, and voltage. In some control applications, it is necessary to command the damper so that it produces desirable control forces calculated based on some optimal control algorithms. In such cases, it is beneficial to develop an inverse dynamic model that estimates the required voltage to be input to the damper so that a desirable damper force can be produced. In this study, we explore such a possibility via the neural network (NN) technique. Recurrent NN models will be constructed to emulate the inverse dynamics of the MR damper. To illustrate the use of these NN models, two control applications will be studied: one is the optimal prediction control of a single-degree-of-freedom system and the other is the linear quadratic regulator control of a multiple-degree-of-freedom system. Numerical results indicate that, using the recurrent NN models, the MR damper force can be commanded to follow closely the desirable optimal control force.
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      Neural Network Emulation of Inverse Dynamics for a Magnetorheological Damper

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    http://yetl.yabesh.ir/yetl1/handle/yetl/33774
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    contributor authorChih-Chen Chang
    contributor authorLi Zhou
    date accessioned2017-05-08T20:58:16Z
    date available2017-05-08T20:58:16Z
    date copyrightFebruary 2002
    date issued2002
    identifier other%28asce%290733-9445%282002%29128%3A2%28231%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/33774
    description abstractThe dynamic behavior of a magnetorheological (MR) damper is well portrayed using a Bouc–Wen hysteresis model. This model estimates damper forces based on the inputs of displacement, velocity, and voltage. In some control applications, it is necessary to command the damper so that it produces desirable control forces calculated based on some optimal control algorithms. In such cases, it is beneficial to develop an inverse dynamic model that estimates the required voltage to be input to the damper so that a desirable damper force can be produced. In this study, we explore such a possibility via the neural network (NN) technique. Recurrent NN models will be constructed to emulate the inverse dynamics of the MR damper. To illustrate the use of these NN models, two control applications will be studied: one is the optimal prediction control of a single-degree-of-freedom system and the other is the linear quadratic regulator control of a multiple-degree-of-freedom system. Numerical results indicate that, using the recurrent NN models, the MR damper force can be commanded to follow closely the desirable optimal control force.
    publisherAmerican Society of Civil Engineers
    titleNeural Network Emulation of Inverse Dynamics for a Magnetorheological Damper
    typeJournal Paper
    journal volume128
    journal issue2
    journal titleJournal of Structural Engineering
    identifier doi10.1061/(ASCE)0733-9445(2002)128:2(231)
    treeJournal of Structural Engineering:;2002:;Volume ( 128 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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