  We present a simple model-free control algorithm that is able to robustly
learn and stabilize an unknown discrete-time linear system with full control
and state feedback subject to arbitrary bounded disturbance and noise
sequences. The controller does not require any prior knowledge of the system
dynamics, disturbances, or noise, yet it can guarantee robust stability and
provides asymptotic and worst-case bounds on the state and input trajectories.
To the best of our knowledge, this is the first model-free algorithm that comes
with such robust stability guarantees without the need to make any prior
assumptions about the system. We would like to highlight the new convex
geometry-based approach taken towards robust stability analysis which served as
a key enabler in our results. We will conclude with simulation results that
show that despite the generality and simplicity, the controller demonstrates
good closed-loop performance.
