Adaptive inferential control

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Abstract
An estimation technique for inferring infrequently measured process outputs, using other more rapidly sampled secondary outputs, is proposed. The estimator has a general structure and its parameters can be estimated online. In contrast to other estimation techniques in the literature, the proposed adaptive method requires minimal design effort and does not result in static estimation error. The problem of secondary output selection is therefore alleviated. Moreover, the estimator can track slow variations in both process and disturbance characteristics. Control schemes using the estimated values of the output are shown to yield considerably better performance than corresponding schemes which employ infrequently sampled values of the primary output.

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