Model Invalidation: A Connection between Robust Control and Identification

This paper begins to address the gap between the models used in robust control theory and those obtained from identification experiments by considering the connection between uncertain models and data. The model invalidation problem considered here is: given experimental data and a model with both additive noise and norm-bounded perturbations, is it possible that the model could produce the input/output data?