On the Effect of Quadratic Regularization in Direct Data-Driven LQR
For researchers in data-driven control, this provides an interpretability framework for quadratic regularization in LQR, but the contribution is incremental as it builds on existing methods.
This paper proposes an explainability concept for direct data-driven LQR with quadratic regularization, translating regularization costs to system quantities for intuitive interpretations and reducing computational complexity. Simulations demonstrate the effectiveness of the approach.
This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect of regularization, an analysis approach that translates regularization costs from auxiliary variables to system quantities, enabling intuitive interpretations. The framework further enables the elimination of auxiliary variables, thereby reducing computational complexity. We demonstrate the effectiveness of our approach and the identified effect of regularization via simulations.