6 papers
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…
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…
Extending direct data-driven predictive control towards systems with finite control sets
Manuel Klädtke, Moritz Schulze Darup, Daniel E. Quevedo
Although classical model predictive control with finite control sets (FCS-MPC) is quite a popular control method, particularly in the realm of power electronics systems, its direct…
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…
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…
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 (…