A self-tuning multistep predictor application

  • Authors:

  • R. M. C. De Keyser;A. R. Van Cauwenberghe

  • Affiliations:

  • Automatic Control Laboratory, University of Ghent, Grote Steenweg Noord 12, B-9710 Gent-Zwijnaarde, Belgium;Automatic Control Laboratory, University of Ghent, Grote Steenweg Noord 12, B-9710 Gent-Zwijnaarde, Belgium

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 1981

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Abstract

A self-tuning multistep predictor is presented. It predicts the output of a stochastic process with unknown, possibly slowly time-varying parameters over a range of several sampling periods in the future. At each sampling instant it is tuned by using a recursive least-squares parameter estimator in real time. By doing this, the combination predictor-estimator converges fast to the optimal predictor for processes with known parameters (self-tuning property). The method seems to have powerful capabilities as an aid in controlling complex industrial processes which are until now only operated under manual control. The predictor can be used by the operator in selecting an appropriate control action (decision making). A typical application, the control of a blast furnace, is extensively dealt with in the paper. The paper opens new perspectives in the domain of self-tuning controllers, and it has practical importance as is indicated by the blast-furnace experiment.