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Real time stabilization of a rotary inverted pendulum using neural networks approach.

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Title: Real time stabilization of a rotary inverted pendulum using neural networks approach.
Authors: Ayad, Mahieddine1 (AUTHOR), Arichi, Fayssal1,2 (AUTHOR) fayssal.arichi@essa-tlemcen.dz, Megnafi, Hicham1,3 (AUTHOR), Merad, Lotfi1,3 (AUTHOR)
Source: Journal of Vibration & Control. Jul2026, Vol. 32 Issue 13/14, p3687-3701. 15p.
Subjects: Inverted pendulum (Control theory), Artificial neural networks, Multilayer perceptrons, Adaptive control systems, Nonlinear systems, Adaptive fuzzy control, Reinforcement learning, Feedback control system stability
Abstract: This paper proposes the use of artificial neural networks methods for the stabilization control of the nonlinear rotary inverted pendulum system. The challenge lies in maintaining balance while keeping the pendulum upright. Three types of controls are proposed for stabilizing the system: multi-layer perceptron control, adaptive network fuzzy inference system control, and deep deterministic policy gradient control. Unlike the first two controllers, the third method achieves stabilization without requiring prior data or hybrid control strategies. An experimental comparison demonstrates the effectiveness and robustness of the proposed model-free controllers. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Vibration & Control is the property of Sage Publications, 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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Header DbId: egs
DbLabel: Engineering Source
An: 194727637
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Real time stabilization of a rotary inverted pendulum using neural networks approach.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Vibration+%26+Control%22">Journal of Vibration & Control</searchLink>. Jul2026, Vol. 32 Issue 13/14, p3687-3701. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Inverted+pendulum+%28Control+theory%29%22">Inverted pendulum (Control theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptrons%22">Multilayer perceptrons</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+fuzzy+control%22">Adaptive fuzzy control</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+control+system+stability%22">Feedback control system stability</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: This paper proposes the use of artificial neural networks methods for the stabilization control of the nonlinear rotary inverted pendulum system. The challenge lies in maintaining balance while keeping the pendulum upright. Three types of controls are proposed for stabilizing the system: multi-layer perceptron control, adaptive network fuzzy inference system control, and deep deterministic policy gradient control. Unlike the first two controllers, the third method achieves stabilization without requiring prior data or hybrid control strategies. An experimental comparison demonstrates the effectiveness and robustness of the proposed model-free controllers. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Vibration & Control is the property of Sage Publications, 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194727637
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/10775463251349249
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 3687
    Subjects:
      – SubjectFull: Inverted pendulum (Control theory)
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Multilayer perceptrons
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Adaptive fuzzy control
        Type: general
      – SubjectFull: Reinforcement learning
        Type: general
      – SubjectFull: Feedback control system stability
        Type: general
    Titles:
      – TitleFull: Real time stabilization of a rotary inverted pendulum using neural networks approach.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Ayad, Mahieddine
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          Name:
            NameFull: Arichi, Fayssal
      – PersonEntity:
          Name:
            NameFull: Megnafi, Hicham
      – PersonEntity:
          Name:
            NameFull: Merad, Lotfi
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          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 10775463
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              Value: 32
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              Value: 13/14
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            – TitleFull: Journal of Vibration & Control
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