Reference modification control DC-DC converter with neural network predictor
The purpose of this paper is to present a new digital control method for dc-dc converters by reference modification with the neural network predictor. In the proposed method, the reference in the proportional control term of the conventional PID control is modified using the neural network predictor...
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| creator | Maruta, H. Motomura, M. Ueno, K. Kurokawa, F. |
| description | The purpose of this paper is to present a new digital control method for dc-dc converters by reference modification with the neural network predictor. In the proposed method, the reference in the proportional control term of the conventional PID control is modified using the neural network predictor during the transient interval. The neural network is repeatedly trained to predict the output voltage using former predicted data for the modification of the reference. After the training, the reference in the P control is modified by the predictor to improve the transient response. By using the proposed method, the undershoot of output voltage is suppressed to 41% compared with the conventional method's one. The convergence time is also improved to 48% compared with the conventional method's one. Therefore, it is confirmed that the proposed method has the superior performance to control dc-dc converters. |
| doi_str_mv | 10.1109/COMPEL.2012.6251806 |
| format | Conference Proceeding |
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In the proposed method, the reference in the proportional control term of the conventional PID control is modified using the neural network predictor during the transient interval. The neural network is repeatedly trained to predict the output voltage using former predicted data for the modification of the reference. After the training, the reference in the P control is modified by the predictor to improve the transient response. By using the proposed method, the undershoot of output voltage is suppressed to 41% compared with the conventional method's one. The convergence time is also improved to 48% compared with the conventional method's one. Therefore, it is confirmed that the proposed method has the superior performance to control dc-dc converters.</description><identifier>ISSN: 1093-5142</identifier><identifier>ISBN: 9781424493722</identifier><identifier>ISBN: 1424493722</identifier><identifier>EISBN: 1424493730</identifier><identifier>EISBN: 1424493714</identifier><identifier>EISBN: 9781424493715</identifier><identifier>EISBN: 9781424493739</identifier><identifier>DOI: 10.1109/COMPEL.2012.6251806</identifier><language>eng</language><publisher>IEEE</publisher><subject>Digital control ; neural network ; Neural networks ; P control ; PD control ; Table lookup ; Training ; Transient analysis ; Transient response</subject><ispartof>2012 IEEE 13th Workshop on Control and Modeling for Power Electronics (COMPEL), 2012, p.1-4</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6251806$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,778,782,787,788,2054,27912,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6251806$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Maruta, H.</creatorcontrib><creatorcontrib>Motomura, M.</creatorcontrib><creatorcontrib>Ueno, K.</creatorcontrib><creatorcontrib>Kurokawa, F.</creatorcontrib><title>Reference modification control DC-DC converter with neural network predictor</title><title>2012 IEEE 13th Workshop on Control and Modeling for Power Electronics (COMPEL)</title><addtitle>COMPEL</addtitle><description>The purpose of this paper is to present a new digital control method for dc-dc converters by reference modification with the neural network predictor. In the proposed method, the reference in the proportional control term of the conventional PID control is modified using the neural network predictor during the transient interval. The neural network is repeatedly trained to predict the output voltage using former predicted data for the modification of the reference. After the training, the reference in the P control is modified by the predictor to improve the transient response. By using the proposed method, the undershoot of output voltage is suppressed to 41% compared with the conventional method's one. The convergence time is also improved to 48% compared with the conventional method's one. Therefore, it is confirmed that the proposed method has the superior performance to control dc-dc converters.</description><subject>Digital control</subject><subject>neural network</subject><subject>Neural networks</subject><subject>P control</subject><subject>PD control</subject><subject>Table lookup</subject><subject>Training</subject><subject>Transient analysis</subject><subject>Transient response</subject><issn>1093-5142</issn><isbn>9781424493722</isbn><isbn>1424493722</isbn><isbn>1424493730</isbn><isbn>1424493714</isbn><isbn>9781424493715</isbn><isbn>9781424493739</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UNtKAzEUjKhgrfsFfckP7JrrZvMo21qFlYr0vWyTE4xuNyWNFv_eFOu8DHOYGZiD0IySilKi79vVy-uiqxihrKqZpA2pL9AtFUwIzRUnl6jQqvnXjF2hSY7xUubTDSoOhw-SkR0ZE9S9gYMIowG8C9Y7b_rkw4hNGFMMA5635bw9qW-ICSI--vSOR_iK_ZApHUP8xPsI1psU4h26dv1wgOLMU7R-XKzbp7JbLZ_bh670mqTSKuk4J04pS4TeMllbw6QSVlBurHa0AUqUpZRtqdQnr2uMqhtuHeRVhk_R7K_WA8BmH_2ujz-b8y_4L2qYUKU</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Maruta, H.</creator><creator>Motomura, M.</creator><creator>Ueno, K.</creator><creator>Kurokawa, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>Reference modification control DC-DC converter with neural network predictor</title><author>Maruta, H. ; Motomura, M. ; Ueno, K. ; Kurokawa, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-d75f330f77d049b256dc2574d413cd9f18e107d112b1595f33f8c7683dfe424c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Digital control</topic><topic>neural network</topic><topic>Neural networks</topic><topic>P control</topic><topic>PD control</topic><topic>Table lookup</topic><topic>Training</topic><topic>Transient analysis</topic><topic>Transient response</topic><toplevel>online_resources</toplevel><creatorcontrib>Maruta, H.</creatorcontrib><creatorcontrib>Motomura, M.</creatorcontrib><creatorcontrib>Ueno, K.</creatorcontrib><creatorcontrib>Kurokawa, F.</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>IEEE Electronic Library (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>Maruta, H.</au><au>Motomura, M.</au><au>Ueno, K.</au><au>Kurokawa, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Reference modification control DC-DC converter with neural network predictor</atitle><btitle>2012 IEEE 13th Workshop on Control and Modeling for Power Electronics (COMPEL)</btitle><stitle>COMPEL</stitle><date>2012-06</date><risdate>2012</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>1093-5142</issn><isbn>9781424493722</isbn><isbn>1424493722</isbn><eisbn>1424493730</eisbn><eisbn>1424493714</eisbn><eisbn>9781424493715</eisbn><eisbn>9781424493739</eisbn><abstract>The purpose of this paper is to present a new digital control method for dc-dc converters by reference modification with the neural network predictor. In the proposed method, the reference in the proportional control term of the conventional PID control is modified using the neural network predictor during the transient interval. The neural network is repeatedly trained to predict the output voltage using former predicted data for the modification of the reference. After the training, the reference in the P control is modified by the predictor to improve the transient response. By using the proposed method, the undershoot of output voltage is suppressed to 41% compared with the conventional method's one. The convergence time is also improved to 48% compared with the conventional method's one. Therefore, it is confirmed that the proposed method has the superior performance to control dc-dc converters.</abstract><pub>IEEE</pub><doi>10.1109/COMPEL.2012.6251806</doi><tpages>4</tpages></addata></record> |
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| identifier | ISSN: 1093-5142 |
| ispartof | 2012 IEEE 13th Workshop on Control and Modeling for Power Electronics (COMPEL), 2012, p.1-4 |
| issn | 1093-5142 |
| language | eng |
| recordid | cdi_ieee_primary_6251806 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Digital control neural network Neural networks P control PD control Table lookup Training Transient analysis Transient response |
| title | Reference modification control DC-DC converter with neural network predictor |
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