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Adaptive stochastic model predictive control via network ensemble learning.

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Title: Adaptive stochastic model predictive control via network ensemble learning.
Authors: Xiong, Weiliang1 (AUTHOR), He, Defeng1 (AUTHOR) hdfzj@zjut.edu.cn, Mu, Jianbin1 (AUTHOR), Wang, Xiuli1 (AUTHOR)
Source: International Journal of Systems Science. Dec2023, Vol. 54 Issue 16, p3013-3026. 14p.
Subjects: Stochastic models, Prediction models, Linear systems, Bayesian analysis, Exponential stability, Machine learning, Adaptive control systems, Feedforward neural networks
Abstract: This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 174099593
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PubTypeId: academicJournal
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  Data: Adaptive stochastic model predictive control via network ensemble learning.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Systems+Science%22">International Journal of Systems Science</searchLink>. Dec2023, Vol. 54 Issue 16, p3013-3026. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+systems%22">Linear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Exponential+stability%22">Exponential stability</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feedforward+neural+networks%22">Feedforward neural networks</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Systems Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/00207721.2023.2268234
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 3013
    Subjects:
      – SubjectFull: Stochastic models
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Linear systems
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Exponential stability
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Feedforward neural networks
        Type: general
    Titles:
      – TitleFull: Adaptive stochastic model predictive control via network ensemble learning.
        Type: main
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          Name:
            NameFull: Xiong, Weiliang
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          Name:
            NameFull: He, Defeng
      – PersonEntity:
          Name:
            NameFull: Mu, Jianbin
      – PersonEntity:
          Name:
            NameFull: Wang, Xiuli
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          Dates:
            – D: 01
              M: 12
              Text: Dec2023
              Type: published
              Y: 2023
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              Value: 54
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              Value: 16
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            – TitleFull: International Journal of Systems Science
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