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    A MinimumControlTrajectoryDeviation Time Grid Reconstruction Strategy for CoDesign Approach

    Source: Journal of Computing and Information Science in Engineering:;2023:;volume( 023 ):;issue: 004::page 41012
    Author:
    Zhang, Jinwen;Li, Congbo;Li, Yongsheng;Wang, Ningbo;Li, Wei
    DOI: 10.1115/1.4056364
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Optimizing dynamic engineering systems (DESs) is quite challenging due to the increasing pursuit of automation and intelligence in modern industry. However, most of the existing studies generally only focus on plant variables or control variables of DESs, which may fail to explore optimal solutions. In this paper, a novel minimumcontroltrajectorydeviation (MCTD) time grid reconstruction strategy is presented for the codesign approach. Three codesign approaches, namely simultaneous, nested, and direct transcription quadratic programming (DTQP) are compared using the MCTD time grid reconstruction strategy. Considering a number of design variables are timevarying in practical dynamic systems, three codesign methods use a special class of numerical analysis methods known as direct transcription (DT) that implies a “discretizethenoptimize” process. Motivated by the inefficiency of the traditional uniform discrete strategy, an MCTD time grid reconstruction strategy is proposed. Combining the presented time grid reconstruction strategy, simultaneous, nested, and DTQP methods are implemented for three test problems. The MCTD time grid reconstruction strategy is verified through a mathematical example, the Van der Pol oscillator, and a machine tool case. All cases have proved the superiority of presented strategy in running cost and solution accuracy.
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      A MinimumControlTrajectoryDeviation Time Grid Reconstruction Strategy for CoDesign Approach

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4288723
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    contributor authorZhang, Jinwen;Li, Congbo;Li, Yongsheng;Wang, Ningbo;Li, Wei
    date accessioned2023-04-06T12:53:44Z
    date available2023-04-06T12:53:44Z
    date copyright1/17/2023 12:00:00 AM
    date issued2023
    identifier issn15309827
    identifier otherjcise_23_4_041012.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288723
    description abstractOptimizing dynamic engineering systems (DESs) is quite challenging due to the increasing pursuit of automation and intelligence in modern industry. However, most of the existing studies generally only focus on plant variables or control variables of DESs, which may fail to explore optimal solutions. In this paper, a novel minimumcontroltrajectorydeviation (MCTD) time grid reconstruction strategy is presented for the codesign approach. Three codesign approaches, namely simultaneous, nested, and direct transcription quadratic programming (DTQP) are compared using the MCTD time grid reconstruction strategy. Considering a number of design variables are timevarying in practical dynamic systems, three codesign methods use a special class of numerical analysis methods known as direct transcription (DT) that implies a “discretizethenoptimize” process. Motivated by the inefficiency of the traditional uniform discrete strategy, an MCTD time grid reconstruction strategy is proposed. Combining the presented time grid reconstruction strategy, simultaneous, nested, and DTQP methods are implemented for three test problems. The MCTD time grid reconstruction strategy is verified through a mathematical example, the Van der Pol oscillator, and a machine tool case. All cases have proved the superiority of presented strategy in running cost and solution accuracy.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA MinimumControlTrajectoryDeviation Time Grid Reconstruction Strategy for CoDesign Approach
    typeJournal Paper
    journal volume23
    journal issue4
    journal titleJournal of Computing and Information Science in Engineering
    identifier doi10.1115/1.4056364
    journal fristpage41012
    journal lastpage4101213
    page13
    treeJournal of Computing and Information Science in Engineering:;2023:;volume( 023 ):;issue: 004
    contenttypeFulltext
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