  In this work, we exploit an offline-sampling based strategy for the
constrained data-driven predictive control of an unknown linear system subject
to random measurement noise. The strategy uses only past measured, potentially
noisy data in a non-parametric system representation and does not require any
prior model identification. The approximation of chance constraints using
uncertainty sampling leads to efficient constraint tightening. Under mild
assumptions, robust recursive feasibility and closed-loop constraint
satisfaction is shown. In a simulation example, we provide evidence for the
improved control performance of the proposed control scheme in comparison to a
purely robust data-driven predictive control approach.
