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AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives.

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Title: AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives.
Authors: Bahar, Saif Talal1 (AUTHOR), Wang, Weilin2 (AUTHOR), Qiu, Hao1,2 (AUTHOR) qh_zju@sina.com
Source: Energies (19961073). Jun2026, Vol. 19 Issue 11, p2574. 24p.
Subject Terms: *Motor drives (Electric motors), *Predictive control systems, *Adaptive control systems, *Parameter estimation, *Torque control, *Permanent magnet motors
Abstract: Permanent magnet synchronous motors (PMSMs) suffer performance degradation under parameter uncertainties and external load disturbances, reducing the effectiveness of conventional proportional-integral and field-oriented control (FOC) schemes. This paper presents an artificial intelligence (AI) enhanced hybrid controller that combines finite-control-set model predictive control (FCS-MPC) and active disturbance rejection control (ADRC). The FCS-MPC optimizes inverter switching states by minimizing a cost function through predicted current trajectories. Additionally, the ADRC employs an extended state observer to estimate and compensate for aggregated disturbances. A lightweight radial basis function neural network is utilized, whose centers and widths are initialized offline based on k-means clustering on representative data, while its output weights are updated online via a Lyapunov-based adaptive law. This network dynamically adjusts the MPC cost function weights and ADRC observer bandwidth according to real-time operating conditions, while enabling online identification of key motor parameters. MATLAB/Simulink R2024a simulations under step load torque conditions verify that the proposed method achieves a speed deviation within 3% of the rated value, an over 90% reduction in torque ripple compared to FOC, and a settling time of less than 5 ms. Although it incurs a moderate computational cost, the proposed controller exhibits improved tracking accuracy and enhanced robustness under simulated conditions. Consequently, the AI-enhanced MPC-ADRC strategy shows strong potential for high-performance applications, subject to future experimental validation. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194587962
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bahar%2C+Saif+Talal%22">Bahar, Saif Talal</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Weilin%22">Wang, Weilin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Hao%22">Qiu, Hao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> qh_zju@sina.com</i>
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Jun2026, Vol. 19 Issue 11, p2574. 24p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Motor+drives+%28Electric+motors%29%22">Motor drives (Electric motors)</searchLink><br />*<searchLink fieldCode="DE" term="%22Predictive+control+systems%22">Predictive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Adaptive+control+systems%22">Adaptive control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br />*<searchLink fieldCode="DE" term="%22Torque+control%22">Torque control</searchLink><br />*<searchLink fieldCode="DE" term="%22Permanent+magnet+motors%22">Permanent magnet motors</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Permanent magnet synchronous motors (PMSMs) suffer performance degradation under parameter uncertainties and external load disturbances, reducing the effectiveness of conventional proportional-integral and field-oriented control (FOC) schemes. This paper presents an artificial intelligence (AI) enhanced hybrid controller that combines finite-control-set model predictive control (FCS-MPC) and active disturbance rejection control (ADRC). The FCS-MPC optimizes inverter switching states by minimizing a cost function through predicted current trajectories. Additionally, the ADRC employs an extended state observer to estimate and compensate for aggregated disturbances. A lightweight radial basis function neural network is utilized, whose centers and widths are initialized offline based on k-means clustering on representative data, while its output weights are updated online via a Lyapunov-based adaptive law. This network dynamically adjusts the MPC cost function weights and ADRC observer bandwidth according to real-time operating conditions, while enabling online identification of key motor parameters. MATLAB/Simulink R2024a simulations under step load torque conditions verify that the proposed method achieves a speed deviation within 3% of the rated value, an over 90% reduction in torque ripple compared to FOC, and a settling time of less than 5 ms. Although it incurs a moderate computational cost, the proposed controller exhibits improved tracking accuracy and enhanced robustness under simulated conditions. Consequently, the AI-enhanced MPC-ADRC strategy shows strong potential for high-performance applications, subject to future experimental validation. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19112574
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 2574
    Subjects:
      – SubjectFull: Motor drives (Electric motors)
        Type: general
      – SubjectFull: Predictive control systems
        Type: general
      – SubjectFull: Adaptive control systems
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Torque control
        Type: general
      – SubjectFull: Permanent magnet motors
        Type: general
    Titles:
      – TitleFull: AI-Enhanced Model Predictive and Active Disturbance Rejection Control for High-Performance Permanent Magnet Synchronous Motor Drives.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Bahar, Saif Talal
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          Name:
            NameFull: Wang, Weilin
      – PersonEntity:
          Name:
            NameFull: Qiu, Hao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
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              Value: 19
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
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