Closed-loop identification of MIMO systems in the Prediction Error framework: Data informativity analysis. (November 2020) - Colin, Kévin; Bombois, Xavier; Bako, Laurent; Morelli, Federico
Abstract: In the Prediction Error identification framework, it is essential that the experiment yields informative data with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the identification of multi-inputs multi-outputs systems in closed-loop and we derive conditions to verify if a given external excitation combined with the feedback introduced by the controller yields informative data with respect to the chosen model structure. This study covers the case of multiple-inputs multiple-outputs model structures that are natural generalizations of the classical single-input single-output model structures used in Prediction Error identification and the classical types of external excitation vectors, i.e., vectors whose elements are either multisine or filtered white noises.