  Data-Enabled Predictive Control (DeePC) bypasses the need for system
identification by directly leveraging raw data to formulate optimal control
policies. However, the size of the optimization problem in DeePC grows linearly
with respect to the data size, which prohibits its application due to high
computational costs. In this paper, we propose an efficient approximation of
DeePC, whose size is invariant with respect to the amount of data collected,
via differentiable convex programming. Specifically, the optimization problem
in DeePC is decomposed into two parts: a control objective and a scoring
function that evaluates the likelihood of a guessed I/O sequence, the latter of
which is approximated with a size-invariant learned optimization problem. The
proposed method is validated through numerical simulations on a quadruple tank
system, illustrating that the learned controller can reduce the computational
time of DeePC by 5x while maintaining its control performance.
