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Adaptive learning control of nonlinear underwater robots: achieving repetitive tracking without parameterization.

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Title: Adaptive learning control of nonlinear underwater robots: achieving repetitive tracking without parameterization.
Authors: Ding, Yaqiong1 (AUTHOR) dingyaqiong@gzmtu.edu.cn, He, Wei1 (AUTHOR) hewei@gzmtu.edu.cn, Zhong, Junliu1 (AUTHOR) yb67402@connect.um.edu.mo, Wan, Kai2 (AUTHOR) wankai606815@hzu.edu.cn, Xiang, Dan1 (AUTHOR) danxiang@gzmtu.edu.cn
Source: ISA Transactions. Aug2026, Vol. 175, p410-420. 11p.
Subjects: Iterative learning control, Differential-difference equations, Robust control, Tracking control systems, Remote submersibles, Nonlinear systems, Adaptive control systems
Abstract: As marine exploration intensifies, precise repetitive tracking control for non-parameterized nonlinear underwater robots is crucial. This paper introduces an adaptive iterative learning control (ILC) approach based on difference-differential principles. By combining difference and differential links, control parameters are dynamically adjusted to achieve high-precision tracking and robustness against system uncertainties. To facilitate memory conservation, the controller features a compact structure requiring only two adaptive variables for parameter updates. Extensive numerical simulations covering various trajectories, parameters, and stochastic disturbances validate the method's effectiveness. Results demonstrate that the proposed controller significantly outperforms existing adaptive ILC schemes in tracking precision and convergence speed, confirming its efficacy for nonlinear underwater robots. • Non-Parametric Nonlinear System Control: This work presents an adaptive repetitive tracking framework designed for underwater robotic systems characterized by non-parametric nonlinearities and continuous dynamics. The approach is capable of effectively compensating for system uncertainties, independent of linear parameterization [4] and any symmetric positive/negative-definite assumptions [29]. • Differential-difference based adaptive ILC Mechanism: A novel ILC mechanism is designed, which harnesses a difference-differential adaptive update law. This design dynamically adjusts control parameters by integrating the respective advantages of difference and differential operations, thereby enabling high-precision tracking control of underwater robotic systems. • Compact Control Architecture Optimization: The implementation of structurally simple adaptive ILC algorithms, requiring only two adaptive variables, significantly lowers computational and memory demands, thereby achieving high performance. [ABSTRACT FROM AUTHOR]
Copyright of ISA Transactions is the property of Elsevier B.V. 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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Header DbId: egs
DbLabel: Engineering Source
An: 195119124
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
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  Data: Adaptive learning control of nonlinear underwater robots: achieving repetitive tracking without parameterization.
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Yaqiong%22">Ding, Yaqiong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dingyaqiong@gzmtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22He%2C+Wei%22">He, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hewei@gzmtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhong%2C+Junliu%22">Zhong, Junliu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yb67402@connect.um.edu.mo</i><br /><searchLink fieldCode="AR" term="%22Wan%2C+Kai%22">Wan, Kai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wankai606815@hzu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiang%2C+Dan%22">Xiang, Dan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> danxiang@gzmtu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ISA+Transactions%22">ISA Transactions</searchLink>. Aug2026, Vol. 175, p410-420. 11p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Iterative+learning+control%22">Iterative learning control</searchLink><br /><searchLink fieldCode="DE" term="%22Differential-difference+equations%22">Differential-difference equations</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+control+systems%22">Tracking control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+submersibles%22">Remote submersibles</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: As marine exploration intensifies, precise repetitive tracking control for non-parameterized nonlinear underwater robots is crucial. This paper introduces an adaptive iterative learning control (ILC) approach based on difference-differential principles. By combining difference and differential links, control parameters are dynamically adjusted to achieve high-precision tracking and robustness against system uncertainties. To facilitate memory conservation, the controller features a compact structure requiring only two adaptive variables for parameter updates. Extensive numerical simulations covering various trajectories, parameters, and stochastic disturbances validate the method's effectiveness. Results demonstrate that the proposed controller significantly outperforms existing adaptive ILC schemes in tracking precision and convergence speed, confirming its efficacy for nonlinear underwater robots. • Non-Parametric Nonlinear System Control: This work presents an adaptive repetitive tracking framework designed for underwater robotic systems characterized by non-parametric nonlinearities and continuous dynamics. The approach is capable of effectively compensating for system uncertainties, independent of linear parameterization [4] and any symmetric positive/negative-definite assumptions [29]. • Differential-difference based adaptive ILC Mechanism: A novel ILC mechanism is designed, which harnesses a difference-differential adaptive update law. This design dynamically adjusts control parameters by integrating the respective advantages of difference and differential operations, thereby enabling high-precision tracking control of underwater robotic systems. • Compact Control Architecture Optimization: The implementation of structurally simple adaptive ILC algorithms, requiring only two adaptive variables, significantly lowers computational and memory demands, thereby achieving high performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ISA Transactions is the property of Elsevier B.V. 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:
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      – Type: doi
        Value: 10.1016/j.isatra.2026.04.029
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 11
        StartPage: 410
    Subjects:
      – SubjectFull: Iterative learning control
        Type: general
      – SubjectFull: Differential-difference equations
        Type: general
      – SubjectFull: Robust control
        Type: general
      – SubjectFull: Tracking control systems
        Type: general
      – SubjectFull: Remote submersibles
        Type: general
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
    Titles:
      – TitleFull: Adaptive learning control of nonlinear underwater robots: achieving repetitive tracking without parameterization.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Ding, Yaqiong
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            NameFull: He, Wei
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            NameFull: Zhong, Junliu
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            NameFull: Wan, Kai
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            NameFull: Xiang, Dan
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            – D: 01
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
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              Value: 175
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            – TitleFull: ISA Transactions
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