Closed-loop issues in system identification

The identification of dynamical systems on the basis of data, measured under closed-loop experimental conditions, is a problem which is highly relevant in many (industrial) applications. Initiated by an emerging interest in the area called 'identification for control', classical prediction error identification methods have been extended to also handle the problem of identifying approximate models from closed-loop observations. In this paper the several procedures that have resulted from this research are reviewed and their characteristic properties are compared. Additionally it is discussed which role closed-loop identification can play in the identification of (optimal) models for (robust) control design.