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Sensorless Direct Field-Oriented Control of Induction Motor Drive Using Artificial Neural Network-Based Reactive Power MRAS

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259576" target="_blank" >RIV/61989100:27240/25:10259576 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.mdpi.com/1424-8220/25/23/7135" target="_blank" >https://www.mdpi.com/1424-8220/25/23/7135</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s25237135" target="_blank" >10.3390/s25237135</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Sensorless Direct Field-Oriented Control of Induction Motor Drive Using Artificial Neural Network-Based Reactive Power MRAS

  • Original language description

    In this paper, advanced sensorless control methods of an induction machine (IM) with the use of a model reference adaptive system (MRAS) estimator are presented, specifically an MRAS using reactive power (Q-MRAS) with an implemented feedforward artificial neural network. Advantages of the proposed solution in comparison with the conventional Q-MRAS include better robustness, less dependence on the IM parameters, and stable operation in the regenerative mode. The simulations were performed in the MATLAB Simulink interface to validate the proposed approach. The algorithm was implemented in a single-core TMS320F28335 real-time Digital Signal Controller with a LabVIEW control panel. The experimental results were obtained on a three-phase experimental induction motor drive with a nominal power of 2.2 kW.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2025

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Sensors

  • ISSN

    1424-8220

  • e-ISSN

    1424-8220

  • Volume of the periodical

    25

  • Issue of the periodical within the volume

    23

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    21

  • Pages from-to

    nestránkováno

  • UT code for WoS article

    001635502400001

  • EID of the result in the Scopus database