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    Stochastic Model Predictive Control for Quasi-Linear Parameter Varying Systems: Case Study on Automotive Engine Control

    Source: Journal of Dynamic Systems, Measurement, and Control:;2022:;volume( 144 ):;issue: 006::page 61005-1
    Author:
    Chen, Kaian
    ,
    Zhang, Kaixiang
    ,
    Li, Zhaojian
    ,
    Wang, Yan
    ,
    Wu, Kai
    ,
    Kalabić, Uroš V.
    DOI: 10.1115/1.4053887
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents an efficient stochastic model predictive control (SMPC) framework for quasi-linear parameter varying (qLPV) systems. The framework applies to general nonlinear systems that are driven by stochastic additive disturbances and subject to chance constraints. The qLPV form is featured by a composition of a set of linear time-invariant (LTI) models with state-/control-dependent scheduling variables, which can be obtained by the spatial–temporal filtering-based system identification approach developed in our earlier work. The overall framework can then be transformed into a tube-based MPC optimization problem which can be efficiently handled by a series of quadratic programing (QP) problems. A case study on automotive engine control is presented as a pilot demonstration of the proposed qLPV–SMPC where we show its advantage over the zone-based MPC, much greater computational efficiency than nonlinear MPC (NMPC) and less conservativeness of the proposed method as compared to its robust MPC (RMPC) counterpart.
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      Stochastic Model Predictive Control for Quasi-Linear Parameter Varying Systems: Case Study on Automotive Engine Control

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4284706
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    • Journal of Dynamic Systems, Measurement, and Control

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    contributor authorChen, Kaian
    contributor authorZhang, Kaixiang
    contributor authorLi, Zhaojian
    contributor authorWang, Yan
    contributor authorWu, Kai
    contributor authorKalabić, Uroš V.
    date accessioned2022-05-08T09:04:52Z
    date available2022-05-08T09:04:52Z
    date copyright3/18/2022 12:00:00 AM
    date issued2022
    identifier issn0022-0434
    identifier otherds_144_06_061005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4284706
    description abstractThis paper presents an efficient stochastic model predictive control (SMPC) framework for quasi-linear parameter varying (qLPV) systems. The framework applies to general nonlinear systems that are driven by stochastic additive disturbances and subject to chance constraints. The qLPV form is featured by a composition of a set of linear time-invariant (LTI) models with state-/control-dependent scheduling variables, which can be obtained by the spatial–temporal filtering-based system identification approach developed in our earlier work. The overall framework can then be transformed into a tube-based MPC optimization problem which can be efficiently handled by a series of quadratic programing (QP) problems. A case study on automotive engine control is presented as a pilot demonstration of the proposed qLPV–SMPC where we show its advantage over the zone-based MPC, much greater computational efficiency than nonlinear MPC (NMPC) and less conservativeness of the proposed method as compared to its robust MPC (RMPC) counterpart.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleStochastic Model Predictive Control for Quasi-Linear Parameter Varying Systems: Case Study on Automotive Engine Control
    typeJournal Paper
    journal volume144
    journal issue6
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4053887
    journal fristpage61005-1
    journal lastpage61005-9
    page9
    treeJournal of Dynamic Systems, Measurement, and Control:;2022:;volume( 144 ):;issue: 006
    contenttypeFulltext
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