YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Dynamic Systems, Measurement, and Control
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Dynamic Systems, Measurement, and Control
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design

    Source: Journal of Dynamic Systems, Measurement, and Control:;2024:;volume( 146 ):;issue: 004::page 41002-1
    Author:
    Nash, Austin L.
    DOI: 10.1115/1.4064772
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Control co-design (CCD) offers a promising solution for coordinating plant and control design of complex systems to better meet next generation demands. Most CCD algorithms optimize open-loop control signals that solve the problem with a single horizon, yet yield system designs lacking robustness to uncertainties. Recent work has integrated modern model predictive control (MPC) policies into CCD. While this results in systems that are more robust, the recursive nature of receding-horizon MPC is computationally expensive and necessitates a bi-level (nested) optimization process to solve sequential MPC problems over smaller horizons. In this work, I present a single-level predictive control co-design (pCCD) optimization framework that approximates the solution to optimizing a recursive MPC within CCD within a single optimization horizon without the need for nested optimization. The pCCD framework leverages elements of static gain matrices as decision variables to integrate a predictive controller into the algorithm that approximates the benefits afforded by embedding a MPC policy in CCD. The formulation reduces algorithm computational complexity by optimizing over the entire operating horizon at once while retaining key robustness and constraint-handling advantages of MPC. Through a comparative case study for a dual-tank thermal management system, this work shows the pCCD algorithm yields superior robustness to disturbance uncertainties compared to an analogous open-loop CCD system while converging on an optimal system/control design with a 92% reduction in run time compared to an analogous system optimized using a recursive MPC policy within the same CCD algorithm.
    • Download: (1.140Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Predictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4302798
    Collections
    • Journal of Dynamic Systems, Measurement, and Control

    Show full item record

    contributor authorNash, Austin L.
    date accessioned2024-12-24T18:48:59Z
    date available2024-12-24T18:48:59Z
    date copyright3/13/2024 12:00:00 AM
    date issued2024
    identifier issn0022-0434
    identifier otherds_146_04_041002.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4302798
    description abstractControl co-design (CCD) offers a promising solution for coordinating plant and control design of complex systems to better meet next generation demands. Most CCD algorithms optimize open-loop control signals that solve the problem with a single horizon, yet yield system designs lacking robustness to uncertainties. Recent work has integrated modern model predictive control (MPC) policies into CCD. While this results in systems that are more robust, the recursive nature of receding-horizon MPC is computationally expensive and necessitates a bi-level (nested) optimization process to solve sequential MPC problems over smaller horizons. In this work, I present a single-level predictive control co-design (pCCD) optimization framework that approximates the solution to optimizing a recursive MPC within CCD within a single optimization horizon without the need for nested optimization. The pCCD framework leverages elements of static gain matrices as decision variables to integrate a predictive controller into the algorithm that approximates the benefits afforded by embedding a MPC policy in CCD. The formulation reduces algorithm computational complexity by optimizing over the entire operating horizon at once while retaining key robustness and constraint-handling advantages of MPC. Through a comparative case study for a dual-tank thermal management system, this work shows the pCCD algorithm yields superior robustness to disturbance uncertainties compared to an analogous open-loop CCD system while converging on an optimal system/control design with a 92% reduction in run time compared to an analogous system optimized using a recursive MPC policy within the same CCD algorithm.
    publisherThe American Society of Mechanical Engineers (ASME)
    titlePredictive Control Co-Design: A Single-Level Optimization Framework for Computationally-Efficient Approximation of Recursive Model Predictive Control in Control Co-Design
    typeJournal Paper
    journal volume146
    journal issue4
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4064772
    journal fristpage41002-1
    journal lastpage41002-9
    page9
    treeJournal of Dynamic Systems, Measurement, and Control:;2024:;volume( 146 ):;issue: 004
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian