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Sensorless control of variable speed induction motor drive using RBF neural network

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238428" target="_blank" >RIV/61989100:27240/17:10238428 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1016/j.jal.2016.11.017" target="_blank" >http://dx.doi.org/10.1016/j.jal.2016.11.017</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.jal.2016.11.017" target="_blank" >10.1016/j.jal.2016.11.017</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Sensorless control of variable speed induction motor drive using RBF neural network

  • Original language description

    High power of modern digital signal processors and their decreasing prices enable practical implementation of different speed estimators which are used in the sensorless control of AC drives. The paper describes application possibilities of artificial neural networks for the sensorless speed control of the A.C. induction motor drive. In the sensorless control structure of the A.C. drive, there is implemented the speed estimator which uses two different artificial neural networks for speed estimation. The first speed estimator uses a multilayer feedforward artificial neural network. Its properties are compared with the speed estimator using a radial basis function neural network. The sensorless A.C. drive was simulated in program Matlab-Simulink. The main goal of many simulations was finding suitable structure of the artificial neural network with required number of neuron units which will ensure good control characteristics and simultaneously will enable a practical implementation of the artificial neural network in the digital signal processor control system.

  • 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

    <a href="/en/project/TE02000103" target="_blank" >TE02000103: Center for Intelligent Drives and Advanced Machine Control (CIDAM)</a><br>

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Journal of Applied Logic

  • ISSN

    1570-8683

  • e-ISSN

  • Volume of the periodical

    24

  • Issue of the periodical within the volume

    November 2017

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    12

  • Pages from-to

    97-108

  • UT code for WoS article

    000413130000010

  • EID of the result in the Scopus database

    2-s2.0-85006991047