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Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer.

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Title: Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer.
Authors: Feng, Fu1,2 (AUTHOR), Yang, Jie1,2 (AUTHOR), Hu, Hailin1,3 (AUTHOR) huhailin06@163.com, Liang, Jianbin1,2 (AUTHOR), Yu, Shiyan1,2 (AUTHOR)
Source: Transactions of the Institute of Measurement & Control. May2026, Vol. 48 Issue 8, p1433-1444. 12p.
Subjects: Linear induction motors, Kalman filtering, Sensorless control systems, Nonlinear difference equations, Predictive control systems, Adaptive estimation (Statistics), Thrust
Abstract: This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance. [ABSTRACT FROM AUTHOR]
Copyright of Transactions of the Institute of Measurement & 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.)
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DbLabel: Engineering Source
An: 193597826
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer.
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  Data: <searchLink fieldCode="AR" term="%22Feng%2C+Fu%22">Feng, Fu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Jie%22">Yang, Jie</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Hailin%22">Hu, Hailin</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> huhailin06@163.com</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Jianbin%22">Liang, Jianbin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Shiyan%22">Yu, Shiyan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Transactions+of+the+Institute+of+Measurement+%26+Control%22">Transactions of the Institute of Measurement & Control</searchLink>. May2026, Vol. 48 Issue 8, p1433-1444. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Linear+induction+motors%22">Linear induction motors</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Sensorless+control+systems%22">Sensorless control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+difference+equations%22">Nonlinear difference equations</searchLink><br /><searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+estimation+%28Statistics%29%22">Adaptive estimation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Thrust%22">Thrust</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper puts forth a strategy for sensorless finite-set model predictive thrust control (FS-MPTC) of linear induction motors (LIM) based on an improved extended Kalman filter (IEKF) observer. It is designed to address the shortcomings of existing LIM sensorless vector control methodologies, namely their lack of robustness and ineffective decoupling due to dynamic end effects. First, a fifth-order nonlinear discrete model of LIM is established in order to meet the requirements of sensorless FS-MPTC. An extended Kalman filter (EKF) observer based on the state-space equations is derived with the objective of achieving synchronous online observation of current, flux linkage and speed. In order to address the limitations of the traditional EKF, which employs a fixed system noise covariance matrix, this paper introduces an adaptive adjustment mechanism based on the difference between the theoretical predictions and actual measurements of the observer. This mechanism allows for the covariance matrix Q to be adjusted in real-time. The enhanced IEKF observer is then put forth. Simulation and experimental outcomes illustrate that, in comparison to the conventional EKF, the IEKF is capable of effectively adapting to internal noise fluctuations resulting from disparate operational conditions, system disturbances and LIM dynamic end effects. It is able to accurately estimate speed and flux linkage, operating stably under both high and low-speed conditions with commendable dynamic and robust performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Transactions of the Institute of Measurement & 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.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1177/01423312241307713
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1433
    Subjects:
      – SubjectFull: Linear induction motors
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Sensorless control systems
        Type: general
      – SubjectFull: Nonlinear difference equations
        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Adaptive estimation (Statistics)
        Type: general
      – SubjectFull: Thrust
        Type: general
    Titles:
      – TitleFull: Speed-sensorless model predictive thrust control of linear induction motor based on improved extended Kalman filter observer.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Feng, Fu
      – PersonEntity:
          Name:
            NameFull: Yang, Jie
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            NameFull: Hu, Hailin
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            NameFull: Liang, Jianbin
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            NameFull: Yu, Shiyan
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 48
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              Value: 8
          Titles:
            – TitleFull: Transactions of the Institute of Measurement & Control
              Type: main
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