Informative data and identifiability in LPV-ARX prediction-error identification.

In system identification, the concepts of informative data and identifiable model structures are important for addressing the statistical properties of estimated models. In this paper, these two concepts are generalized from the classical LTI prediction-error identification framework to the situation of LPV model structures and appropriate definitions are introduced. For two particular cases (piecewise constant and periodic scheduling trajectories) conditions are derived for the data sets to be informative w.r.t. the LPV-ARX model structure. Moreover, conditions are derived under which the LPV-ARX model structure is globally identifiable. [ABSTRACT FROM PUBLISHER]