  On the wave of recent advances in data-driven predictive control, we present
an explicit predictive controller that can be constructed from a batch of
input/output data only. The proposed explicit law is build upon a regularized
implicit data-driven predictive control problem, so as to guarantee the
uniqueness of the explicit predictive controller. As a side benefit, the use of
regularization is shown to improve the capability of the explicit law in coping
with noise on the data. The effectiveness of the retrieved explicit law and the
repercussions of regularization on noise handling are analyzed on two benchmark
simulation case studies, showing the potential of the proposed regularized
explicit controller.
