  Starting from a linear fractional representation of a linear system affected
by constant parametric uncertainties, we demonstrate how to enhance standard
robust analysis tests by taking available (noisy) input-output data of the
uncertain system into account. Our approach relies on lifting the system and
the construction of data-dependent multipliers. It leads to a test in terms of
linear matrix inequalities which guarantees stability and performance for all
systems compatible with the observed data if it is in the affirmative. In
contrast to many other data-based approaches, prior physical or structural
knowledge about the system can be incorporated at the outset by exploiting the
power of linear fractional representations.
