Guaranteed Performances Design via Model Sets Identification

Abstract

In this paper an integrated robust identification and control design procedure is proposed. It is supposed that the plant to be controlled is linear, time invariant, stable, possibly infinite dimensional and that input-output noise-corrupted measurements are available. The emphasis is placed on the design of controllers guaranteeing robust stability and robust performances, and on the trade off between controller complexity and achievable robust performances. First, uncertainty models are identified, consisting of parametric models of different order and tight frequency bounds on the magnitude of the unmodeled dynamics. Second, Internal Model Controllers, guaranteeing robust closed loop stability and best approximating the “perfect control” ideal target, are designed using µ-synthesis techniques. Then, the robust performances of the designed controllers are computed, allowing to determine the level of model/controller complexity needed to guarantee desired closed loop performances.