  Model Predictive Control (MPC) is a powerful method for complex system
regulation, but its reliance on an accurate model poses many limitations in
real-world applications. Data-driven predictive control (DDPC) aims at
overcoming this limitation, by relying on historical data to provide
information on the plant to be controlled. In this work, we present a unified
stochastic framework for direct DDPC, where control actions are obtained by
optimizing the Final Control Error (FCE), which is directly computed from
available data only and automatically weighs the impact of uncertainty on the
control objective. Our framework allows us to establish a separation principle
for Predictive Control, elucidating the role that predictive models and their
uncertainty play in DDPC. Moreover, it generalizes existing DDPC methods, like
regularized Data-enabled Predictive Control (DeePC) and $\gamma$-DDPC,
providing a path toward noise-tolerant data-based control with rigorous
optimality guarantees. The theoretical investigation is complemented by a
series of numerical case studies, revealing that the proposed method
consistently outperforms or, at worst, matches existing techniques without
requiring tuning regularization parameters as other methods do.
