  In the field of model predictive control, Data-enabled Predictive Control
(DeePC) offers direct predictive control, bypassing traditional modeling.
However, challenges emerge with increased computational demand due to recursive
data updates. This paper introduces a novel recursive updating algorithm for
DeePC. It emphasizes the use of Singular Value Decomposition (SVD) for
efficient low-dimensional transformations of DeePC in its general form, as well
as a fast SVD update scheme. Importantly, our proposed algorithm is highly
flexible due to its reliance on the general form of DeePC, which is
demonstrated to encompass various data-driven methods that utilize
Pseudoinverse and Hankel matrices. This is exemplified through a comparison to
Subspace Predictive Control, where the algorithm achieves asymptotically
consistent prediction for stochastic linear time-invariant systems. Our
proposed methodologies' efficacy is validated through simulation studies.
