  We consider the problem of designing robust state-feedback controllers for
discrete-time linear time-invariant systems, based directly on measured data.
The proposed design procedures require no model knowledge, but only a single
open-loop data trajectory, which may be affected by noise. First, a data-driven
characterization of the uncertain class of closed-loop matrices under
state-feedback is derived. By considering this parametrization in the robust
control framework, we design data-driven state-feedback gains with guarantees
on stability and performance, containing, e.g., the
$\mathcal{H}_\infty$-control problem as a special case. Further, we show how
the proposed framework can be extended to take partial model knowledge into
account. The validity of the proposed approach is illustrated via a numerical
example.
