  For a partially unknown linear systems, we present a systematic control
design approach based on generated data from measurements of closed-loop
experiments with suitable test controllers. These experiments are used to
improve the achieved performance and to reduce the uncertainty about the
unknown parts of the system. This is achieved through a parametrization of
auspicious controllers with convex relaxation techniques from robust control,
which guarantees that their implementation on the unknown plant is safe. This
approach permits to systematically incorporate available prior knowledge about
the system by employing the framework of linear fractional representations.
