Towards a unifying framework for data-driven predictive control with quadratic regularization

TL;DR

This paper proposes a unifying theoretical framework for data-driven predictive control methods, clarifying their relationships and enabling transfer of results across different approaches.

Contribution

It provides a formal characterization of various DPC frameworks, specifically analyzing the connection between γ-DDPC and DeePC, to unify and streamline their understanding.

Findings

Established formal relationships between DPC frameworks

Demonstrated the connection between γ-DDPC and DeePC

Facilitated transfer of results across frameworks

Abstract

Data-driven predictive control (DPC) has recently gained popularity as an alternative to model predictive control (MPC). Amidst the surge in proposed DPC frameworks, upon closer inspection, many of these frameworks are more closely related (or perhaps even equivalent) to each other than it may first appear. We argue for a more formal characterization of these relationships so that results can be freely transferred from one framework to another, rather than being uniquely attributed to a particular framework. We demonstrate this idea by examining the connection between $\gamma$-DDPC and the original DeePC formulation.