  Model predictive control (MPC) is a control strategy widely used in
industrial applications. However, its implementation typically requires a
mathematical model of the system being controlled, which can be a
time-consuming and expensive task. Data-driven predictive control (DDPC)
methods offer an alternative approach that does not require an explicit
mathematical model, but instead optimize the control policy directly from data.
In this paper, we study the impact of two different regularization penalties on
the closed-loop performance of a recently introduced data-driven method called
$\gamma$-DDPC. Moreover, we discuss the tuning of the related coefficients in
different data and noise scenarios, to provide some guidelines for the end
user.
