An estimator relationship for inferring infrequently measured process outputs, from other more rapidly sampled secondary outputs, is derived. The structure of the estimator is not application dependent, and its parameters can be continuously estimated and updated. As a result, slow variations in plant or disturbance characteristics can be tracked. Closed-loop control schemes using the estimated output data are shown to exhibit superior performance over those using the slowly measured output data. In contrast to some other inferential control strategies suggested in the literature, the method proposed requires minimal effort for estimator design and secondary output selection.
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