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    Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

    Source: Journal of Dynamic Systems, Measurement, and Control:;2012:;volume( 134 ):;issue: 004::page 41008
    Author:
    Lei Pan
    ,
    Jiong Shen
    ,
    Peter B. Luh
    DOI: 10.1115/1.4006085
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.
    keyword(s): Algorithms , Predictive control , Sampling (Acoustical engineering) , Boilers AND Control equipment ,
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      Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

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    contributor authorLei Pan
    contributor authorJiong Shen
    contributor authorPeter B. Luh
    date accessioned2017-05-09T00:49:08Z
    date available2017-05-09T00:49:08Z
    date copyrightJuly, 2012
    date issued2012
    identifier issn0022-0434
    identifier otherJDSMAA-26589#041008_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/148471
    description abstractAn adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAdaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header
    typeJournal Paper
    journal volume134
    journal issue4
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4006085
    journal fristpage41008
    identifier eissn1528-9028
    keywordsAlgorithms
    keywordsPredictive control
    keywordsSampling (Acoustical engineering)
    keywordsBoilers AND Control equipment
    treeJournal of Dynamic Systems, Measurement, and Control:;2012:;volume( 134 ):;issue: 004
    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