Identification of experimental models for control design

The identification of linear models that are particularly suitable for serving as a basis for (robust) model-based control design has recently attracted considerable attention. Both the system identification community and the control community have spent considerable efforts in developing a coherent approach to the problem. Typical problems that have to be dealt with consider questions of optimal experiment design, feedback-relevant system approximations and control-relevant model uncertainty specifications. Research into these problems has delivered several attempts for bridging the gap between identification and control theory. In this lecture these developments are highlighted, directing particular attention to the identification of control-relevant approximate models, the use of closed-loop experimental data for identification, the quantification of model uncertainty, and the use of identification criteria that are motivated by control performance cost functions.