Model validation in l₁ using frequency-domain data

摘要

In this paper we study the problem of invalidating uncertain models with an additive uncertainty. The problem is to check the existence of an uncertainty and a measurement noise which fit to the given model structure and the uncertaintyoise description, as well as the experimental data used for invalidation. We consider a mixed setting in which the uncertainty is characterized in time domain by the l₁ induced system norm, while the available data are frequency response samples of the system. We show that this problem, which by formulation poses an infinite-dimensional primal optimization problem, can be solved in a dual, finite-dimensional space with finitely many constraints.