System Identification for Robust Control

In the design of a robust control system, one needs a nominal model together with a quantitative bound on the uncertainty that results from under-modeling and disturbances. Thus, common control-oriented system identification methods deliver an optimal model, meanwhile the uncertainty is characterized with a bound which is usually rather conservative. Alternatively, in this thesis we do not intentionally seek a nominal model and a quantitative bound, instead, the uncertainty is directly parameterized so that the resulting uncertain model family can be characterized by means of a real parameter vector with at most unit length. In other words, the family can be characterized in terms of an ellipsoid. This is an innovative approach to the control-oriented system identification, since it is not in accordance with the general philosophy of robust identification. However, it is applicable to the robust synthesis problem by taking advantage of a convex parameterization of robust controllers th..