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Closed-Loop Identification

  • Biao Huang
  • Sirish L. Shah
Part of the Advances in Industrial Control book series (AIC)

Abstract

A necessary prerequisite for model-based control is a model of the process. Such certainty-equivalence, model-based control schemes rely on an off-line estimated model of the process, i.e., the process is “probed” or excited by a carefully designed input-signal under open-loop conditions and the input-output data are used to generate a suitable model of the process. In a majority of model-based control schemes used in the chemical process industry, the models are generated with little regard for their ultimate end-use, e.g., as in model-predictive control. Almost always, in such cases, reduced-complexity models are generated to capture the most dominant dynamics of the process. Such batch or off-line identification methods represent a major effort and may require anywhere from several hours to several weeks of open-loop tests.

Keywords

Sensitivity Function Bias Error Shaping Filter Residual Test Bias Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 1999

Authors and Affiliations

  • Biao Huang
    • 1
  • Sirish L. Shah
    • 1
  1. 1.Department of Chemical and Materials EngineeringUniversity of AlbertaEdmontonCanada

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