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A Cautious Approach to Robust Design with Model Uncertainty

By Daniel W. Apley and Jeongbae Kim


Robust design methods rely on empirical process models that relate an output response variable to a set controllable input variables and uncontrollable noise variables. Model uncertainty is typically neglected, however, when determining the input settings that minimize output variability about a target. Using a Bayesian problem formulation similar to what has been used in cautious adaptive control, this paper develops a cautious robust design method that takes model uncertainty into account. The result is a relatively simple, closed-form expression for the robust design objective function that involves both the parameter estimates and their covariance information. The approach is cautious in the sense that the input settings are generally smaller, more cautious values as parameter uncertainty increases

Topics: Cautious Control, Model Uncertainty, Bayesian Estimation
Year: 2002
OAI identifier: oai:CiteSeerX.psu:
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