  Recently proposed data-driven predictive control schemes for LTI systems use
non-parametric representations based on the image of a Hankel matrix of
previously collected, persistently exciting, input-output data. Persistence of
excitation necessitates that the data is sufficiently long and, hence, the
computational complexity of the corresponding finite-horizon optimal control
problem increases. In this paper, we propose an efficient data-driven
predictive control (eDDPC) scheme which is both more sample efficient (requires
less offline data) and computationally efficient (uses less decision variables)
compared to existing schemes. This is done by leveraging an alternative
data-based representation of the trajectories of LTI systems. We analytically
and numerically compare the performance of this scheme to existing ones from
the literature.
