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FPGA based neural network position and speed estimator for switched reluctance motor drive

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F08%3A00502869" target="_blank" >RIV/49777513:23220/08:00502869 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    FPGA based neural network position and speed estimator for switched reluctance motor drive

  • Original language description

    This paper presents the design and implementation of the sensorless control system for the Switched Reluctance Motor (SRM) drive without a position sensor. The SRM has been receiving attention for industry applications due to its low cost in mass production, reduced maintenance requirements, rugged behavior and large torque Output over very high speed range. On the other hand, torque ripple, acoustic noise and rotor position sensor requirements are the main disadvantages. This work has been supported bythe Czech Ministry of Education - project: Modern Control of the Switched Reluctance Motor Drives - 913/2008/G1.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JA - Electronics and optoelectronics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2008

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Circuits, Systems, Electronics, Control and Signal Processing

  • ISBN

    978-960-474-035-2

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

  • Publisher name

    WSEAS Press

  • Place of publication

    Athens

  • Event location

  • Event date

  • Type of event by nationality

  • UT code for WoS article

    000264171900023