Quadratic Regularization of Data-Enabled Predictive Control: Theory and Application to Power Converter Experiments
OPEN ACCESS
Loading...
Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Abstract
Data-driven control that circumvents the process of system identification by providing optimal control inputs directly from system data has attracted renewed attention in recent years. In this paper, we focus on understanding the effects of the regularization on the data-enabled predictive control (DeePC) algorithm. We provide theoretical motivation and interpretation for including a quadratic regularization term. Our analysis shows that the quadratic regularization term leads to robust and optimal solutions with regards to disturbances affecting the data. Moreover, when the input/output constraints are inactive, the quadratic regularization leads to a closed-form solution of the DeePC algorithm and thus enables fast calculations. On this basis, we propose a framework for data-driven synchronization and power regulations of power converters, which is tested by high-fidelity simulations and experiments.
Permanent link
Publication status
published
External links
Book title
19th IFAC Symposium on System Identification, SYSID 2021
Journal / series
Volume
54 (7)
Pages / Article No.
192 - 197
Publisher
Elsevier
Event
19th IFAC Symposium on System Identification (SYSID 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Data-driven control; predictive control; robust optimization; regularization; power converters
Organisational unit
03751 - Lygeros, John / Lygeros, John
09478 - Dörfler, Florian / Dörfler, Florian
Notes
Funding
787845 - Optimal control at large (EC)