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  <titleInfo>
    <title>Nonlinear control and filtering for stochastic networked systems</title>
  </titleInfo>
  <name type="personal">
    <namePart>Ma, Lifeng</namePart>
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  <name type="personal">
    <namePart>Wang, Zidong</namePart>
    <namePart type="date">1966-</namePart>
    <role>
      <roleTerm type="text">author.</roleTerm>
    </role>
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  <name type="personal">
    <namePart>Bo, Yuming</namePart>
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    <dateIssued encoding="marc">2019</dateIssued>
    <copyrightDate encoding="marc">2019</copyrightDate>
    <issuance>monographic</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
    <extent>1 online resource (xv, 226 pages) : illustrations (black and white)</extent>
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  <abstract>In this book, control and filtering problems for several classes of stochastic networked systems are discussed. In each chapter, the stability, robustness, reliability, consensus performance, and/or disturbance attenuation levels are investigated within a unified theoretical framework. The aim is to derive the sufficient conditions such that the resulting systems achieve the prescribed design requirements despite all the network-induced phenomena. Further, novel notions such as randomly occurring sensor failures and consensus in probability are discussed. Finally, the theories/techniques developed are applied to emerging research areas. Key Features Unifies existing and emerging concepts concerning stochastic control/filtering and distributed control/filtering with an emphasis on a variety of network-induced complexities Includes concepts like randomly occurring sensor failures and consensus in probability (with respect to time-varying stochastic multi-agent systems) Exploits the recursive linear matrix inequality approach, completing the square method, Hamilton-Jacobi inequality approach, and parameter-dependent matrix inequality approach to handle the emerging mathematical/computational challenges Captures recent advances of theories, techniques, and applications of stochastic control as well as filtering from an engineering-oriented perspective Gives simulation examples in each chapter to reflect the engineering practice.</abstract>
  <tableOfContents>Introduction -- Robust H</tableOfContents>
  <note type="statement of responsibility">Lifeng Ma, Zidong Wang, Yuming Bo.</note>
  <subject authority="lcsh">
    <topic>Stochastic systems</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Nonlinear control theory</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Filters (Mathematics)</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>SCIENCE</topic>
    <topic>System Theory</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>TECHNOLOGY &amp; ENGINEERING</topic>
    <topic>Operations Research</topic>
  </subject>
  <classification authority="lcc">QA402 .M287 2019eb</classification>
  <classification authority="ddc" edition="23">003.76</classification>
  <identifier type="isbn">9780429761928</identifier>
  <identifier type="isbn">0429761929</identifier>
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  <identifier type="isbn">0429761937</identifier>
  <identifier type="isbn">9780429761911</identifier>
  <identifier type="isbn">0429761910</identifier>
  <identifier type="isbn">9780429426759</identifier>
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