Accelerating Control Design for Quasi-Resonant Mode Boost Converters through State Machine Modelling Approach

The design of digital controllers for boost converters operating in quasi-resonant mode (QRM) is often hindered by the trade-off between accurately modelling dynamic behavior and efficiently analyzing frequency-domain characteristics. To address this challenge, this paper introduces a novel modelling approach that leverages a state machine to provide a highly accurate and computationally efficient model. By achieving a balance between precision and speed, this method enables faster simulation and design of digital controllers, while maintaining comparable simulation accuracy, thereby enhancing overall design efficiency and effectiveness.

Authors

Institutions

Publication Details

Published
2025-06-30
DOI
https://doi.org/10.1109/icca65672.2025.11129809
Primary Topic
Semiconductor materials and devices
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accelerating Control Design for Quasi-Resonant Mode Boost Converters through State Machine Modelling Approach

Lorenzo Servadei, Benjamin Schwabe, Zhi Li
Semiconductor materials and devices
article

Accelerating Control Design for Quasi-Resonant Mode Boost Converters through State Machine Modelling Approach

Lorenzo Servadei, Benjamin Schwabe, Zhi Li
article en

Abstract

The design of digital controllers for boost converters operating in quasi-resonant mode (QRM) is often hindered by the trade-off between accurately modelling dynamic behavior and efficiently analyzing frequency-domain characteristics. To address this challenge, this paper introduces a novel modelling approach that leverages a state machine to provide a highly accurate and computationally efficient model. By achieving a balance between precision and speed, this method enables faster simulation and design of digital controllers, while maintaining comparable simulation accuracy, thereby enhancing overall design efficiency and effectiveness.

Infineon Technologies (Germany) (DE), Technical University of Munich (DE)
Openalex Percentile: Top 78%
Semiconductor materials and devices
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.