  We present a novel tube-based data-driven predictive control method for
linear systems affected by a bounded addictive disturbance. Our method
leverages recent results in the reachability analysis of unknown linear systems
to formulate and solve a robust tube-based predictive control problem. More
precisely, our approach consists in deriving, from the collected data, a
zonotope that includes the true state error set. We show how to guarantee the
stability of the resulting error zonotope, which can be exploited to increase
the computational efficiency of existing zonotopic data-driven MPC
formulations. Results on a double-integrator affected by strong adversarial
noise demonstrate the effectiveness of the proposed control approach.
