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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...
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creator | Kostov, K.S. Kyyra, J.J. |
description | 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_str_mv | 10.1109/SMCIA.2005.1466957 |
format | conference_proceeding |
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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.</description><identifier>ISBN: 9780780389427</identifier><identifier>ISBN: 0780389425</identifier><identifier>DOI: 10.1109/SMCIA.2005.1466957</identifier><language>eng</language><publisher>IEEE</publisher><subject>Buck converters ; Control systems ; Genetic algorithms ; Optimization methods ; Pulse width modulation converters ; Switched-mode power supply ; Switching converters ; Testing ; Transfer functions ; Voltage control</subject><ispartof>Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. 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SMCia/05</title><addtitle>SMCIA</addtitle><description>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.</description><subject>Buck converters</subject><subject>Control systems</subject><subject>Genetic algorithms</subject><subject>Optimization methods</subject><subject>Pulse width modulation converters</subject><subject>Switched-mode power supply</subject><subject>Switching converters</subject><subject>Testing</subject><subject>Transfer functions</subject><subject>Voltage control</subject><isbn>9780780389427</isbn><isbn>0780389425</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj99KwzAchQMiKLMvoDd5gdb8T3M5is7Bhhfq9UjTXzSubUqaCfr0VtzhwOG7OfAhdEtJRSkx9y_7ZruuGCGyokIpI_UFKoyuyVJeG8H0FSrm-ZMsEZJrqq_RfgMj5OCw7d9jCvljwHHKYQg_Noc44ujxBPaI3SklGDMeYgfYxTGn2PfQ4fbkjn_8BSlDukGX3vYzFOddobfHh9fmqdw9b7bNelcGqmUuu1p5AGWcb7XhXBBGqe8YY9YZIJIzzxTVWjPhDXXCtNYzKxcB0dWi5YKv0N3_bwCAw5TCYNP34SzNfwHMnE21</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Kostov, K.S.</creator><creator>Kyyra, J.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2005</creationdate><title>Genetic algorithm optimization of peak current mode controlled buck converter</title><author>Kostov, K.S. ; Kyyra, J.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d86fee69cfb793340211fd222ac9e0532f26177724f91c49baf2a59424d84b343</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Buck converters</topic><topic>Control systems</topic><topic>Genetic algorithms</topic><topic>Optimization methods</topic><topic>Pulse width modulation converters</topic><topic>Switched-mode power supply</topic><topic>Switching converters</topic><topic>Testing</topic><topic>Transfer functions</topic><topic>Voltage control</topic><toplevel>online_resources</toplevel><creatorcontrib>Kostov, K.S.</creatorcontrib><creatorcontrib>Kyyra, J.J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kostov, K.S.</au><au>Kyyra, J.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Genetic algorithm optimization of peak current mode controlled buck converter</atitle><btitle>Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05</btitle><stitle>SMCIA</stitle><date>2005</date><risdate>2005</risdate><spage>111</spage><epage>116</epage><pages>111-116</pages><isbn>9780780389427</isbn><isbn>0780389425</isbn><abstract>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.</abstract><pub>IEEE</pub><doi>10.1109/SMCIA.2005.1466957</doi><tpages>6</tpages></addata></record> |
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subjects | Buck converters Control systems Genetic algorithms Optimization methods Pulse width modulation converters Switched-mode power supply Switching converters Testing Transfer functions Voltage control |
title | Genetic algorithm optimization of peak current mode controlled buck converter |
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