Optimal asymptotic identification under bounded disturbances

A general framework for analyzing asymptotically optimal algorithms and experiment designs for worst-case identification is described. The authors develop the framework and state the general results. These are then applied to analyze three specific identification problems of stable and unstable plants.<<ETX>>