Quadratic Regularization of Data-Enabled Predictive Control: Theory and Application to Power Converter Experiments
| Autor: | Linbin Huang, John Lygeros, Jianzhe Zhen, Florian Dörfler |
|---|---|
| Přispěvatelé: | Pillonetto, Gianluigi |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science power converters robust optimization Systems and Control (eess.SY) 010103 numerical & computational mathematics 02 engineering and technology Electrical Engineering and Systems Science - Systems and Control 01 natural sciences Regularization (mathematics) 020901 industrial engineering & automation Quadratic equation Control theory Synchronization (computer science) FOS: Electrical engineering electronic engineering information engineering 0101 mathematics Data-driven control System identification Robust optimization Optimal control Term (time) regularization Model predictive control Control and Systems Engineering predictive control |
| Zdroj: | 19th IFAC Symposium on System Identification, SYSID 2021 IFAC-PapersOnLine, 54 (7) |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2021.08.357 |
| Popis: | 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. IFAC-PapersOnLine, 54 (7) ISSN:2405-8963 19th IFAC Symposium on System Identification, SYSID 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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