Abstract
This paper studies identification in H∞ based on multiple sets of data in the frequency domain. The traditional H∞ worst-case identification and the stochastic identification settings are revisited, and a new problem formulation is presented by modifying the H∞ worst-case identification setting. In the literature, given a single set of data, the limited experimental information subject to finite samples has been focused on, while this paper considers uncertainty modeling given multiple sets of data. It is shown that the problem is solved by means of linear matrix inequalities and subspace identification methods in the frequency domain.
