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Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems.

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Title: Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems.
Authors: Taimoor, Muhammad1,2 (AUTHOR) muhtaimoor123@hotmail.com, Lu, Xiao1,3 (AUTHOR) luxiao98@163.com, Shabbir, Wasif4 (AUTHOR) wasifshabbir@hotmail.com, Sheng, Chunyang1 (AUTHOR) scy@sdust.edu.cn
Source: Expert Systems with Applications. Mar2024:Part A, Vol. 237, pN.PAG-N.PAG. 1p.
Subjects: Nonlinear systems, Sliding mode control, Drone aircraft, Fault diagnosis, Online education, Iterative learning control, Adaptive fuzzy control
Abstract: This research suggests the use of neural network observers based on fuzzy auxiliary sliding mode control for the fault estimation and isolation of quadrotor unmanned aerial vehicle sensors. The fuzzy auxiliary sliding-mode-control-based adaptive approach for neural network observer is used for the approximation, reconstruction, and isolation of unknown faults by utilizing the multi-layer neural network. The neural network weight parameters are updated adaptively by using the fuzzy auxiliary sliding-mode-control approach. In conventional neural networks, the gradient descent approach-based back-propagation procedures are adapted for the training of the neural network. In this research, a new concept of the nonlinear controller such as the fuzzy auxiliary sliding-mode-control method is adapted for neural network online training, in which the fuzzy auxiliary sliding-mode-control is used as the learning approach; the neural network is used as a control process that calculates the stable and dynamic learning rates. By the consideration of unknown faults diagnosis and isolation, the online learning approach used in this research has shown the faults estimation, reconstruction, and isolation abruptly and with high accuracy compared to conventional approaches as well the approach used in literature. Strategies adopted in the literature are not capable of fault detection, estimation, and reconstruction with high accuracy and abruptness compared to the method adopted in this research. The presented approach is validated by using the nonlinear dynamics of quadrotor unmanned aerial vehicles, results show the accuracy, abruptness, and efficiency compared to the algorithms adapted in the literature. It is suggested that the proposed strategy can be integrated into nonlinear systems fault diagnosis, fault isolation, and for increasing the system performance. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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
FullText Text:
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Header DbId: egs
DbLabel: Engineering Source
An: 173705923
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems.
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  Data: <searchLink fieldCode="AR" term="%22Taimoor%2C+Muhammad%22">Taimoor, Muhammad</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> muhtaimoor123@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Xiao%22">Lu, Xiao</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> luxiao98@163.com</i><br /><searchLink fieldCode="AR" term="%22Shabbir%2C+Wasif%22">Shabbir, Wasif</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> wasifshabbir@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Sheng%2C+Chunyang%22">Sheng, Chunyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> scy@sdust.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>. Mar2024:Part A, Vol. 237, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Nonlinear+systems%22">Nonlinear systems</searchLink><br /><searchLink fieldCode="DE" term="%22Sliding+mode+control%22">Sliding mode control</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+learning+control%22">Iterative learning control</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+fuzzy+control%22">Adaptive fuzzy control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This research suggests the use of neural network observers based on fuzzy auxiliary sliding mode control for the fault estimation and isolation of quadrotor unmanned aerial vehicle sensors. The fuzzy auxiliary sliding-mode-control-based adaptive approach for neural network observer is used for the approximation, reconstruction, and isolation of unknown faults by utilizing the multi-layer neural network. The neural network weight parameters are updated adaptively by using the fuzzy auxiliary sliding-mode-control approach. In conventional neural networks, the gradient descent approach-based back-propagation procedures are adapted for the training of the neural network. In this research, a new concept of the nonlinear controller such as the fuzzy auxiliary sliding-mode-control method is adapted for neural network online training, in which the fuzzy auxiliary sliding-mode-control is used as the learning approach; the neural network is used as a control process that calculates the stable and dynamic learning rates. By the consideration of unknown faults diagnosis and isolation, the online learning approach used in this research has shown the faults estimation, reconstruction, and isolation abruptly and with high accuracy compared to conventional approaches as well the approach used in literature. Strategies adopted in the literature are not capable of fault detection, estimation, and reconstruction with high accuracy and abruptness compared to the method adopted in this research. The presented approach is validated by using the nonlinear dynamics of quadrotor unmanned aerial vehicles, results show the accuracy, abruptness, and efficiency compared to the algorithms adapted in the literature. It is suggested that the proposed strategy can be integrated into nonlinear systems fault diagnosis, fault isolation, and for increasing the system performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science 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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    Identifiers:
      – Type: doi
        Value: 10.1016/j.eswa.2023.121492
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Nonlinear systems
        Type: general
      – SubjectFull: Sliding mode control
        Type: general
      – SubjectFull: Drone aircraft
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Online education
        Type: general
      – SubjectFull: Iterative learning control
        Type: general
      – SubjectFull: Adaptive fuzzy control
        Type: general
    Titles:
      – TitleFull: Neural network observer based on fuzzy auxiliary sliding-mode-control for nonlinear systems.
        Type: main
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          Name:
            NameFull: Taimoor, Muhammad
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            NameFull: Lu, Xiao
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            NameFull: Shabbir, Wasif
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            NameFull: Sheng, Chunyang
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          Dates:
            – D: 01
              M: 03
              Text: Mar2024:Part A
              Type: published
              Y: 2024
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              Value: 237
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            – TitleFull: Expert Systems with Applications
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