  The frequency-domain data of a multivariable system in different operating
points is used to design a robust controller with respect to the measurement
noise and multimodel uncertainty. The controller is fully parametrized in terms
of matrix polynomial functions and can be formulated as a centralized,
decentralized or distributed controller. All standard performance
specifications like $H_2$, $H_\infty$ and loop shaping are considered in a
unified framework for continuous- and discrete-time systems. The control
problem is formulated as a convex-concave optimization problem and then
convexified by linearization of the concave part around an initial controller.
The performance criterion converges monotonically to a local optimal solution
in an iterative algorithm. The effectiveness of the method is compared with
fixed-structure controllers using non-smooth optimization and with full-order
optimal controllers via simulation examples. Finally, the experimental data of
a gyroscope is used to design a data-driven controller that is successfully
applied on the real system.
