View in EDS HTML Full Text PDF Full Text

Sensorless Control of Compressor Motor Considering Inverter Nonlinearities and Parameter Estimation.

Saved in:
Bibliographic Details
Title: Sensorless Control of Compressor Motor Considering Inverter Nonlinearities and Parameter Estimation.
Authors: Sapmaz, Tunahan1 (AUTHOR) tsapmaz@yildiz.edu.tr, Bakan, Ahmet Faruk1 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2374. 27p.
Subject Terms: *Sensorless control systems, *Parameter estimation, *Artificial neural networks, *Least squares, *Air compressors, *Observability (Control theory)
Abstract: In this study, parameter estimation-assisted sensorless control methods are proposed for compressor motors. As sensorless control strategies, rotating high-frequency injection (RHFI), pulsating high-frequency injection (RHFI), and an adaptive-gain sliding mode observer (AG-SMO) are employed. During startup, HFI-based methods are utilized, whereas AG-SMO is activated under steady-state operating conditions. To mitigate parameter variations and inverter nonlinearities, Adaline Neural Network (ANN), Recursive Least Squares (RLS), and Extended Kalman Filter (EKF) algorithms are integrated for the real-time estimation of stator resistance and dead-time voltage. The proposed framework is validated through both simulation and experimental studies on a 30 W, 20 V interior permanent magnet motor commonly used in compressor applications. The results demonstrate that sensorless control algorithms alone provide robust operation, while the incorporation of parameter estimation effectively eliminates stability issues and ensures reliable transitions from low to high speeds. Comparative analysis reveals that ANN has a simple structure, RLS achieves faster convergence, and EKF provides smoother estimates under noisy conditions. Overall, the integration of sensorless control algorithms with ANN/RLS/EKF-based parameter estimation and dead-time compensation offers a cost-effective and reliable solution for high-performance compressor applications. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194141489
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Sensorless Control of Compressor Motor Considering Inverter Nonlinearities and Parameter Estimation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sapmaz%2C+Tunahan%22">Sapmaz, Tunahan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tsapmaz@yildiz.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Bakan%2C+Ahmet+Faruk%22">Bakan, Ahmet Faruk</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2374. 27p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Sensorless+control+systems%22">Sensorless control systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br />*<searchLink fieldCode="DE" term="%22Air+compressors%22">Air compressors</searchLink><br />*<searchLink fieldCode="DE" term="%22Observability+%28Control+theory%29%22">Observability (Control theory)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In this study, parameter estimation-assisted sensorless control methods are proposed for compressor motors. As sensorless control strategies, rotating high-frequency injection (RHFI), pulsating high-frequency injection (RHFI), and an adaptive-gain sliding mode observer (AG-SMO) are employed. During startup, HFI-based methods are utilized, whereas AG-SMO is activated under steady-state operating conditions. To mitigate parameter variations and inverter nonlinearities, Adaline Neural Network (ANN), Recursive Least Squares (RLS), and Extended Kalman Filter (EKF) algorithms are integrated for the real-time estimation of stator resistance and dead-time voltage. The proposed framework is validated through both simulation and experimental studies on a 30 W, 20 V interior permanent magnet motor commonly used in compressor applications. The results demonstrate that sensorless control algorithms alone provide robust operation, while the incorporation of parameter estimation effectively eliminates stability issues and ensures reliable transitions from low to high speeds. Comparative analysis reveals that ANN has a simple structure, RLS achieves faster convergence, and EKF provides smoother estimates under noisy conditions. Overall, the integration of sensorless control algorithms with ANN/RLS/EKF-based parameter estimation and dead-time compensation offers a cost-effective and reliable solution for high-performance compressor applications. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141489
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en19102374
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 2374
    Subjects:
      – SubjectFull: Sensorless control systems
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Least squares
        Type: general
      – SubjectFull: Air compressors
        Type: general
      – SubjectFull: Observability (Control theory)
        Type: general
    Titles:
      – TitleFull: Sensorless Control of Compressor Motor Considering Inverter Nonlinearities and Parameter Estimation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sapmaz, Tunahan
      – PersonEntity:
          Name:
            NameFull: Bakan, Ahmet Faruk
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19961073
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Energies (19961073)
              Type: main
ResultId 1