Experiment design for grey box identification

Abstract

AbstractGrey box models are characterized by their physical significance e.g. in parametrization and by the partial prior information that is available about e.g. the parameter values. These aspects of the grey box model affect the design of optimal excitations for identification and we study the extension of classical theory for experiment design to input design for identification of grey box models. Partial prior information is expressed as a probability distribution and is employed in the design of optimal excitations through optimization of Bayesian criteria.