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    Neural Network Based Tracking Control of Mechanical Systems

    Source: Journal of Dynamic Systems, Measurement, and Control:;1999:;volume( 121 ):;issue: 001::page 148
    Author:
    T. Efrati
    ,
    H. Flashner
    DOI: 10.1115/1.2802435
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.
    keyword(s): Artificial neural networks , Tracking control , Control equipment , Algorithms , Optimization , Closed loop systems , Computation , Errors AND Feedforward control ,
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      Neural Network Based Tracking Control of Mechanical Systems

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    http://yetl.yabesh.ir/yetl1/handle/yetl/121958
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    • Journal of Dynamic Systems, Measurement, and Control

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    contributor authorT. Efrati
    contributor authorH. Flashner
    date accessioned2017-05-08T23:59:18Z
    date available2017-05-08T23:59:18Z
    date copyrightMarch, 1999
    date issued1999
    identifier issn0022-0434
    identifier otherJDSMAA-26252#148_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/121958
    description abstractA method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleNeural Network Based Tracking Control of Mechanical Systems
    typeJournal Paper
    journal volume121
    journal issue1
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.2802435
    journal fristpage148
    journal lastpage154
    identifier eissn1528-9028
    keywordsArtificial neural networks
    keywordsTracking control
    keywordsControl equipment
    keywordsAlgorithms
    keywordsOptimization
    keywordsClosed loop systems
    keywordsComputation
    keywordsErrors AND Feedforward control
    treeJournal of Dynamic Systems, Measurement, and Control:;1999:;volume( 121 ):;issue: 001
    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
    yabeshDSpacePersian