A cautious minimum variance controller with ARIMA disturbances

This article investigates a Cautious Minimum Variance (CMV) control approach for controlling industrial process variability when the model parameters are estimated from data and subject to uncertainty. CMV control has a number of advantages over traditional robust control methods. It incorporates probabilistic, as opposed to deterministic, measures of parameter uncertainty, which are more consistent with the statistical methods typically used to estimate industrial process models. CMV control is also more consistent with the objective of minimizing process variability, since parameter uncertainty is treated simply as an additional source of variation. CMV results have previously been derived for the case where the process disturbance follows a first-order integrated moving average model. This work extends the results to autoregressive moving average and autoregressive integrated moving average disturbances.