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A novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense for batch processes.

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Title: A novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense for batch processes.
Authors: Li, Haisheng1 (AUTHOR) haishengsd@163.com, Chen, Yu1 (AUTHOR), Han, Bin2 (AUTHOR), Zou, Hongbo3 (AUTHOR)
Source: Asian Journal of Control. Nov2025, Vol. 27 Issue 6, p2996-3015. 20p.
Subjects: Iterative learning control, Batch processing, Two-dimensional models, Mathematical optimization, State feedback (Feedback control systems), Process optimization
Abstract: Although linear quadratic iterative learning control methods based on nonminimal state space (NMSS) models can directly observe the system state without designing a state observer, there is still room for improvement in their control performance. In order to further improve the control performance of core process indicators for batch processes, this paper presents a novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense (2D‐IHLQILC). Firstly, an extended nonminimal state space (2D‐ENMSS) model in two‐dimensional sense is developed to describe a batch process by incorporating process inputs, outputs, and tracking errors. Secondly, a 2D‐IHLQILC scheme is designed based on this 2D‐ENMSS process model. Finally, the effectiveness of the 2D‐IHLQILC scheme is demonstrated by the injection speed control in injection modeling process. Compared with conventional methods based on NMSS model, the proposed 2D‐IHLQILC method provides more extra degrees of freedom (DOF) to adjust the control performance and acquires improved control performance. In addition, it does not need a state observer to observe the system state. [ABSTRACT FROM AUTHOR]
Copyright of Asian Journal of Control is the property of Wiley-Blackwell 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.)
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  Data: A novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense for batch processes.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Haisheng%22">Li, Haisheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> haishengsd@163.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yu%22">Chen, Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Han%2C+Bin%22">Han, Bin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zou%2C+Hongbo%22">Zou, Hongbo</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. Nov2025, Vol. 27 Issue 6, p2996-3015. 20p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Iterative+learning+control%22">Iterative learning control</searchLink><br /><searchLink fieldCode="DE" term="%22Batch+processing%22">Batch processing</searchLink><br /><searchLink fieldCode="DE" term="%22Two-dimensional+models%22">Two-dimensional models</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22State+feedback+%28Feedback+control+systems%29%22">State feedback (Feedback control systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Although linear quadratic iterative learning control methods based on nonminimal state space (NMSS) models can directly observe the system state without designing a state observer, there is still room for improvement in their control performance. In order to further improve the control performance of core process indicators for batch processes, this paper presents a novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense (2D‐IHLQILC). Firstly, an extended nonminimal state space (2D‐ENMSS) model in two‐dimensional sense is developed to describe a batch process by incorporating process inputs, outputs, and tracking errors. Secondly, a 2D‐IHLQILC scheme is designed based on this 2D‐ENMSS process model. Finally, the effectiveness of the 2D‐IHLQILC scheme is demonstrated by the injection speed control in injection modeling process. Compared with conventional methods based on NMSS model, the proposed 2D‐IHLQILC method provides more extra degrees of freedom (DOF) to adjust the control performance and acquires improved control performance. In addition, it does not need a state observer to observe the system state. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Asian Journal of Control is the property of Wiley-Blackwell 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: 10.1002/asjc.3634
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 2996
    Subjects:
      – SubjectFull: Iterative learning control
        Type: general
      – SubjectFull: Batch processing
        Type: general
      – SubjectFull: Two-dimensional models
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: State feedback (Feedback control systems)
        Type: general
      – SubjectFull: Process optimization
        Type: general
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      – TitleFull: A novel infinite horizon linear quadratic iterative learning control strategy in two‐dimensional sense for batch processes.
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          Name:
            NameFull: Li, Haisheng
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            NameFull: Chen, Yu
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            NameFull: Han, Bin
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            NameFull: Zou, Hongbo
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          Dates:
            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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              Value: 27
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              Value: 6
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            – TitleFull: Asian Journal of Control
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