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  <titleInfo>
    <title>Speed control of buck-converter driven DC motor using PD-type fuzzy logic controller</title>
  </titleInfo>
  <titleInfo type="alternative">
    <title>Speed control of buck-converter driven DC motor using PD-type fuzzy logic controller</title>
  </titleInfo>
  <name type="personal">
    <namePart>Zakaria Abdul Rahman</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">theses</genre>
  <originInfo>
    <place>
      <placeTerm type="code" authority="marccountry">my</placeTerm>
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    <place>
      <placeTerm type="text">Kuantan, Pahang</placeTerm>
    </place>
    <publisher>UMP</publisher>
    <dateIssued>2009</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <form authority="gmd">electronic resource</form>
    <extent>48 p. : ill. (some col.) ; 30 cm. + 1 computer disc</extent>
  </physicalDescription>
  <abstract>The purpose of this project is to control speed of buck converter driven DC motor using PD-type fuzzy logic controller. At the beginning, the simulation (MATLAB simulink) is started with buck converter driven DC motor modeling. In this project, PD-type fuzzy logic controller is designed based on the membership function and the rule base. Thus, the designed PD-type fuzzy logic is applied to the buck converter driven DC motor model. The objective of the simulation is to predict the system response of the buck converter driven DC motor with different membership function. For the first model of PD-type fuzzy logic controller, it will use 3 membership functions which are equal to 9 rule base. Then for the second simulation, it will use 5 membership functions which are equal to 25 rule base and the last model of controller use 7 membership function that are equal to 49 rules. Fuzzy logic controller that is capable of improving its performance in the control of a nonlinear system whose dynamics is unknown or uncertain. This direct learning fuzzy controller is able to improve its performance without having to identify a model of the plant.</abstract>
  <targetAudience authority="marctarget">specialized</targetAudience>
  <note type="statement of responsibility">Zakaria Bin Abdul Rahman</note>
  <note>Project paper (Bachelor of Electrical Engineering (Electronics)) -- Universiti Malaysia Pahang - 2009</note>
  <subject authority="lcsh">
    <topic>Fuzzy logic</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Programmable logic controllers</topic>
  </subject>
  <identifier type="isbn">THE0006997(Local)</identifier>
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    <recordContentSource authority="marcorg">UMP</recordContentSource>
    <recordCreationDate encoding="marc">090721</recordCreationDate>
    <recordChangeDate encoding="iso8601">20251114204405.0</recordChangeDate>
    <recordIdentifier source="KUKTEM">vtls000040633</recordIdentifier>
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