Data informativity for the open-loop identification of MIMO systems in the prediction error framework. (July 2020) - Colin, Kévin; Bombois, Xavier; Bako, Laurent; Morelli, Federico
Abstract: In Prediction Error identification, to obtain a consistent estimate of the true system, it is crucial that the input excitation yields informative data with respect to the chosen model structure. We consider in this paper the data informativity property for the identification of a Multiple-Input Multiple-Output system in open-loop and we derive conditions to check whether a given input vector will yield informative data with respect to the chosen model structure. We do that for the classical model structures used in prediction error identification and for the classical types of input vectors, i.e., input vectors whose elements are either multisines or filtered white noises.