Résumé : The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focus on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modelling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this work focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems.
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