  Data-driven control that circumvents the process of system identification by
providing optimal control inputs directly from system data has attracted
renewed attention in recent years. In this paper, we focus on understanding the
effects of the regularization on the data-enabled predictive control (DeePC)
algorithm. We provide theoretical motivation and interpretation for including a
quadratic regularization term. Our analysis shows that the quadratic
regularization term leads to robust and optimal solutions with regards to
disturbances affecting the data. Moreover, when the input/output constraints
are inactive, the quadratic regularization leads to a closed-form solution of
the DeePC algorithm and thus enables fast calculations. On this basis, we
propose a framework for data-driven synchronization and power regulations of
power converters, which is tested by high-fidelity simulations and experiments.
