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20222026
most citedImplicit predictors in regularized data-driven predictive control

8 citations · 11 across the 7 of their papers we have counts for

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7 papers · 1 filter

eess.SY2026

On the Effect of Quadratic Regularization in Direct Data-Driven LQR

Manuel Klädtke, Feiran Zhao, Florian Dörfler +1

This paper proposes an explainability concept for direct data-driven linear quadratic regulation (LQR) with quadratic regularization. Our perspective follows the parametric effect…

eess.SY2025★ 1 cited

On data usage and predictive behavior of data-driven predictive control with 1-norm regularization

Manuel Klädtke, Moritz Schulze Darup

We investigate the data usage and predictive behavior of data-driven predictive control (DPC) with 1-norm regularization. Our analysis enables the offline removal of unused data an…

eess.SY2025

Towards explainable data-driven predictive control with regularizations

Manuel Klädtke, Moritz Schulze Darup

Data-driven predictive control (DPC), using linear combinations of recorded trajectory data, has recently emerged as a popular alternative to traditional model predictive control (…

eess.SY2024★ 2 cited

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

Manuel Klädtke, Moritz Schulze Darup

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 in…

eess.SY2023★ 8 cited

Implicit predictors in regularized data-driven predictive control

Manuel Klädtke, Moritz Schulze Darup

We introduce the notion of implicit predictors, which characterize the input-(state)-output prediction behavior underlying a predictive control scheme, even if it is not explicitly…

eess.SY2023

Convex NMPC reformulations for a special class of nonlinear multi-input systems with application to rank-one bilinear networks

Manuel Klädtke, Moritz Schulze Darup

We show that a special class of (nonconvex) NMPC problems admits an exact solution by reformulating them as a finite number of convex subproblems, extending previous results to the…

eess.SY2022

Convex reformulations for a special class of nonlinear MPC problems

Manuel Klädtke, Moritz Schulze Darup

We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control proble…