Genetic algorithm optimization of peak current mode controlled buck converter

This paper presents a genetic algorithm (GA), which optimizes the parameters of an analog controller for switched mode power converter (SMPC). A peak current mode controlled buck converter is used to test the optimization algorithm. The SMPC's response to line and load step changes is simulated...

Full description

Saved in:
Bibliographic Details
Main Authors: Kostov, K.S., Kyyra, J.J.
Format: Conference Proceeding
Language:English
Subjects:
Buck converters
Control systems
Genetic algorithms
Optimization methods
Pulse width modulation converters
Switched-mode power supply
Switching converters
Testing
Transfer functions
Voltage control
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a genetic algorithm (GA), which optimizes the parameters of an analog controller for switched mode power converter (SMPC). A peak current mode controlled buck converter is used to test the optimization algorithm. The SMPC's response to line and load step changes is simulated with every combination of controller parameters emerging in GA's population. Each controller, i.e. each chromosome in the population, is assigned a cost depending on the simulated performance of the converter. The algorithm converges successfully. Although it relies on simulations, the measurements confirm that the controllers obtained by the GA result in a SMPC with stable and fast response with minimum over- and under-shoot. This method of controller optimization requires an accurate and reliable simulation model, but the transfer functions of the converter are not needed. Therefore, it can be most useful, if converter's continuous transfer function model is unknown, or if traditional controller design techniques do not yield satisfactory results.
DOI:10.1109/SMCIA.2005.1466957