• DocumentCode
    1356423
  • Title

    Algorithms for industrial model predictive control

  • Author

    Sandoz, David J. ; Desforges, Matthew J. ; Lennox, Barry ; Goulding, Peter R.

  • Author_Institution
    Sch. of Eng., Manchester Univ., UK
  • Volume
    11
  • Issue
    3
  • fYear
    2000
  • fDate
    6/1/2000 12:00:00 AM
  • Firstpage
    125
  • Lastpage
    134
  • Abstract
    The article is concerned with control methods that have been embedded in an industrial model predictive control software package and that have been applied to a wide variety of industrial processes. Three methods are described and the various features are evaluated by considering a constrained multivariable simulation. One method has been in use since 1988 and is widely exploited in industry. The latest methods employ quadratic programming, which has become realistic to employ because of the advances in computing. The relative attributes are contrasted by assessing the ability of the controllers to recover effectively from the impact of a large unmeasured disturbance.
  • Keywords
    process control; constrained multivariable simulation; control methods; industrial model predictive control algorithms; industrial process control; large unmeasured disturbance; quadratic programming; relative attributes; software package;
  • fLanguage
    English
  • Journal_Title
    Computing & Control Engineering Journal
  • Publisher
    iet
  • ISSN
    0956-3385
  • Type

    jour

  • DOI
    10.1049/cce:20000306
  • Filename
    850787