Abstract
A self-tuning multistep predictor is presented. It predicts the output of a stochastic process with unknown, possibly tiIre-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 caIXiliilities 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. A typical application, the control of a blast furnace, is extensively dealt with in the paper.The paper opens new perspectives in the donain of self-tuning controllers, and it has practical importance as is indicated by the blast-furnace experiment.
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