  In this work, we introduce a novel data-driven model-reference control design
approach for unknown linear systems with fully measurable state. The proposed
control action is composed by a static feedback term and a reference tracking
block, which are shaped from data to reproduce the desired behavior in
closed-loop. By focusing on the case where the reference model and the plant
share the same order, we propose an optimal design procedure with Lyapunov
stability guarantees, tailored to handle state measurements with additive
noise. Two simulation examples are finally illustrated to show the potential of
the proposed strategy as compared to the state of the art approaches.
