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    CMAC Neural Network Control for High Precision Motion Control in the Presence of Large Friction

    Source: Journal of Dynamic Systems, Measurement, and Control:;1995:;volume( 117 ):;issue: 003::page 415
    Author:
    G. A. Larsen
    ,
    A. Donmez
    ,
    S. Cetinkunt
    DOI: 10.1115/1.2799133
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Precision requirements in ultra-precision machining are often given in the order of micrometers or sub-micrometers. Machining at these levels requires precise control of the position and speed of the machine tool axes. Furthermore, in machining of brittle materials, extremely low feed rates of the machine tool axes are required. At these low feed rates there is a large and erratic friction characteristic in the drive system which standard PID controllers are unable to deal with. In order to achieve the desired accuracies, friction must be accurately compensated in the real-time servo control algorithm. A learning controller based on the CMAC algorithm is studied for this task.
    keyword(s): Friction , Motion control , Accuracy , Artificial neural networks , Machining , Machine tools , Control equipment , Algorithms , Servomechanisms AND Brittleness ,
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      CMAC Neural Network Control for High Precision Motion Control in the Presence of Large Friction

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/115060
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    contributor authorG. A. Larsen
    contributor authorA. Donmez
    contributor authorS. Cetinkunt
    date accessioned2017-05-08T23:46:47Z
    date available2017-05-08T23:46:47Z
    date copyrightSeptember, 1995
    date issued1995
    identifier issn0022-0434
    identifier otherJDSMAA-26216#415_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/115060
    description abstractPrecision requirements in ultra-precision machining are often given in the order of micrometers or sub-micrometers. Machining at these levels requires precise control of the position and speed of the machine tool axes. Furthermore, in machining of brittle materials, extremely low feed rates of the machine tool axes are required. At these low feed rates there is a large and erratic friction characteristic in the drive system which standard PID controllers are unable to deal with. In order to achieve the desired accuracies, friction must be accurately compensated in the real-time servo control algorithm. A learning controller based on the CMAC algorithm is studied for this task.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleCMAC Neural Network Control for High Precision Motion Control in the Presence of Large Friction
    typeJournal Paper
    journal volume117
    journal issue3
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.2799133
    journal fristpage415
    journal lastpage420
    identifier eissn1528-9028
    keywordsFriction
    keywordsMotion control
    keywordsAccuracy
    keywordsArtificial neural networks
    keywordsMachining
    keywordsMachine tools
    keywordsControl equipment
    keywordsAlgorithms
    keywordsServomechanisms AND Brittleness
    treeJournal of Dynamic Systems, Measurement, and Control:;1995:;volume( 117 ):;issue: 003
    contenttypeFulltext
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