  We propose a purely data-driven online convex optimization algorithm for
controlling dynamical systems. In particular, the proposed control scheme makes
use of an initially measured input-output trajectory and behavioral systems
theory which enable it to handle unknown discrete-time linear time-invariant
systems as well as a priori unknown time-varying cost functions. Further, only
output feedback instead of full state measurements is required for the proposed
approach. Analysis of the closed loop's performance reveals that the algorithm
achieves sublinear regret if the variation of the cost functions is sublinear.
The effectiveness of the proposed algorithm is illustrated by means of a
simulation example.
