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<records>
  <record>
    <language>eng</language>
    <publisher>Science and Education Publishing</publisher>
    <journalTitle>Automatic Control and Information Sciences</journalTitle>
    <publicationDate>2014-03-22</publicationDate>
    <volume>2</volume>
    <issue>1</issue>
    <startPage>26</startPage>
    <endPage>31</endPage>
    <doi>10.12691/acis-2-1-5</doi>
    <publisherRecordId>ACIS2014215</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">A Decentralized Event-Based Model Predictive Controller Design Method for Large-Scale Systems</title>
    <authors>
      <author>
        <name>Karim Salahshoor</name>
        <affiliationId>1</affiliationId>
      </author>
      <author>
        <name>Mohsen Hadian</name>
        <email>m.hadian@put.ac.ir</email>
        <affiliationId>1</affiliationId>
      </author>
    </authors>
    <affiliationsList>
      <affiliationName affiliationId="1">Department of Instrumentation and Industrial Automation, Petroleum University of Technology, Ahwaz, Iran</affiliationName>
    </affiliationsList>
    <abstract language="eng">This paper presents a new methodology to design decentralized event-based control strategy for large-scale systems under the general MPC framework. The method introduces an appealing perspective to effectively reduce the computing load and communication effort in computer-based networks by incorporating the MPC approach in an event-based design framework. The proposed methodology is shown to be capable of coping explicitly with multi-input, multi-output (MIMO) plants having constraints while preserving the control performance characteristics due to decentralized MPC method with less control computational effort. The proposed control architecture ensures the stability of the closed-loop system, optimal performance and significant reduction in computational load without sacrificing the performance. Performances of the proposed method are comparatively explored on a catalytic alkylation of benzene process plant as the benchmark case study. A diverse set of experiments has been conducted to clearly demonstrate superiority of the proposed methodology compared to the standard time-driven decentralized MPC scheme on the basis of mean-squared error and number of events or control actions measures.</abstract>
    <fullTextUrl format="pdf">http://pubs.sciepub.com/acis/2/1/5/acis-2-1-5.pdf</fullTextUrl>
    <keywords language="eng">
      <keyword>event-based control</keyword>
      <keyword>model predictive control</keyword>
      <keyword>large scale system</keyword>
      <keyword>decentralized</keyword>
    </keywords>
  </record>
</records>