On the Effect of Quadratic Regularization in Direct Data-Driven LQR
Systems and Control
2026-04-21 v1 Systems and Control
Optimization and Control
Abstract
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.
Cite
@article{arxiv.2604.18453,
title = {On the Effect of Quadratic Regularization in Direct Data-Driven LQR},
author = {Manuel Klädtke and Feiran Zhao and Florian Dörfler and Moritz Schulze Darup},
journal= {arXiv preprint arXiv:2604.18453},
year = {2026}
}
Comments
This paper is a preprint of a contribution to the 23rd IFAC World Congress 2026. 7 pages, 3 figures