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Adaptive Neural Network Synergetic Control for Nonlinear Hysteretic Systems.

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Title: Adaptive Neural Network Synergetic Control for Nonlinear Hysteretic Systems.
Authors: Zhang, Wenhui1 hit_zwh@126.com, Zhou, Hui2 15261498478@163.com, Chen, Rui3 cr1716707642@126.com, Ma, Jing4 mj2201@126.com
Source: IAENG International Journal of Applied Mathematics. Sep2026, Vol. 56 Issue 9, p4019-4023. 5p.
Subjects: Artificial neural networks, Nonlinear systems, Manipulators (Machinery), Hysteresis, Radial basis functions, Adaptive control systems, Tracking control systems
Abstract: This paper proposes an adaptive neural network-based synergetic control scheme for nonlinear systems with unknown input delays and modeling uncertainties. Radial basis function (RBF) neural networks are employed to approximate unknown dynamics online, reducing the effects of uncertainties and delay-induced disturbances. A macro-variable-based synergetic manifold is designed to improve tracking convergence, while adaptive laws update the network weights online without prior knowledge of system nonlinearities. Lyapunov analysis guarantees the boundedness of all closed-loop signals and the convergence of the tracking error. Simulations on a single-link robotic manipulator verify the effectiveness of the proposed method. Comparative results demonstrate improved tracking accuracy, faster transient response, and stronger robustness than adaptive backstepping control. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) 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: 196779583
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  Data: Adaptive Neural Network Synergetic Control for Nonlinear Hysteretic Systems.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Wenhui%22">Zhang, Wenhui</searchLink><relatesTo>1</relatesTo><i> hit_zwh@126.com</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Hui%22">Zhou, Hui</searchLink><relatesTo>2</relatesTo><i> 15261498478@163.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Rui%22">Chen, Rui</searchLink><relatesTo>3</relatesTo><i> cr1716707642@126.com</i><br /><searchLink fieldCode="AR" term="%22Ma%2C+Jing%22">Ma, Jing</searchLink><relatesTo>4</relatesTo><i> mj2201@126.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Sep2026, Vol. 56 Issue 9, p4019-4023. 5p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Manipulators+%28Machinery%29%22">Manipulators (Machinery)</searchLink><br /><searchLink fieldCode="DE" term="%22Hysteresis%22">Hysteresis</searchLink><br /><searchLink fieldCode="DE" term="%22Radial+basis+functions%22">Radial basis functions</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Tracking+control+systems%22">Tracking control systems</searchLink>
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  Data: This paper proposes an adaptive neural network-based synergetic control scheme for nonlinear systems with unknown input delays and modeling uncertainties. Radial basis function (RBF) neural networks are employed to approximate unknown dynamics online, reducing the effects of uncertainties and delay-induced disturbances. A macro-variable-based synergetic manifold is designed to improve tracking convergence, while adaptive laws update the network weights online without prior knowledge of system nonlinearities. Lyapunov analysis guarantees the boundedness of all closed-loop signals and the convergence of the tracking error. Simulations on a single-link robotic manipulator verify the effectiveness of the proposed method. Comparative results demonstrate improved tracking accuracy, faster transient response, and stronger robustness than adaptive backstepping control. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) 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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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 5
        StartPage: 4019
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Manipulators (Machinery)
        Type: general
      – SubjectFull: Hysteresis
        Type: general
      – SubjectFull: Radial basis functions
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Tracking control systems
        Type: general
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      – TitleFull: Adaptive Neural Network Synergetic Control for Nonlinear Hysteretic Systems.
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            NameFull: Zhang, Wenhui
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            NameFull: Zhou, Hui
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            NameFull: Chen, Rui
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            NameFull: Ma, Jing
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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            – TitleFull: IAENG International Journal of Applied Mathematics
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