A Digital Iterative Learning Based Peak Current Mode Control for Interleaved Totem Pole PFC Circuit

Ahmet Talha Dudak () and Ahmet Faruk Bakan
Additional contact information
Ahmet Talha Dudak: Department of Power and Control Systems Design Engineering, ASELSAN, 06200 Ankara, Turkey
Ahmet Faruk Bakan: Department of Electrical Engineering, Yildiz Technical University, 34220 Istanbul, Turkey

Energies, 2024, vol. 17, issue 20, 1-18

Abstract: Iterative learning based digital peak current mode control (PCMC) is proposed in this paper. The proposed control method provides excellent current reference tracking against variations in input voltage, load, and circuit parameters. Compared to other current control methods, the proposed digital PCMC has a high dynamic response, a simple structure and a low computational burden. It is suitable for power factor correction (PFC) converters operating at high frequency. Thanks to the iterative learning control (ILC), the peak current value in PCMC is successfully compensated against disturbances. The proposed new current control method is applied to an interleaved totem pole PFC (ITPPFC) circuit. The ITPPFC circuit prototype is implemented with 250 W output power and 100 kHz switching frequency. The circuit prototype is tested under various load conditions and parametric disturbances. Theoretical and experimental results are found to be consistent.

Keywords: digital peak current mode control (PCMC); current reference tracking; iterative learning control (ILC); interleaved totem pole PFC (ITPPFC) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/20/5026/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/20/5026/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:20:p:5026-:d:1495455

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().