Automatic Loop-Shaping of QFT Controllers Using GAs and Evolutionary Computation
This paper presents a design method of the automatic loop-shaping which couples up manual loop-shaping method to genetic algorithms (GAs) in quantitative feedback theory (QFT). The loop-shaping is currently performed in computer aided design environments manually, and moreover, it is usually a trial...
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creator | Kim, Min-Soo Chung, Chan-Soo |
description | This paper presents a design method of the automatic loop-shaping which couples up manual loop-shaping method to genetic algorithms (GAs) in quantitative feedback theory (QFT). The loop-shaping is currently performed in computer aided design environments manually, and moreover, it is usually a trial and error procedure. To solve this problem, an automatic loop-shaping method based on GAs and evolutionary computation is developed and a benchmark example is used to examine the performance of the proposed automatic loop-shaping compared with that of the manual loop-shaping and similar other research. |
doi_str_mv | 10.1007/11589990_147 |
format | Conference Proceeding |
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The loop-shaping is currently performed in computer aided design environments manually, and moreover, it is usually a trial and error procedure. To solve this problem, an automatic loop-shaping method based on GAs and evolutionary computation is developed and a benchmark example is used to examine the performance of the proposed automatic loop-shaping compared with that of the manual loop-shaping and similar other research.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540304622</identifier><identifier>ISBN: 9783540304623</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540316523</identifier><identifier>EISBN: 9783540316527</identifier><identifier>DOI: 10.1007/11589990_147</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Frequency Array ; Margin Bound ; Order Controller ; Quantitative Feedback Theory ; Tracking Bound</subject><ispartof>AI 2005: Advances in Artificial Intelligence, 2005, p.1096-1100</ispartof><rights>Springer-Verlag Berlin Heidelberg 2005</rights><rights>2006 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11589990_147$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11589990_147$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,4036,4037,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17345152$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Jarvis, Ray</contributor><contributor>Zhang, Shichao</contributor><creatorcontrib>Kim, Min-Soo</creatorcontrib><creatorcontrib>Chung, Chan-Soo</creatorcontrib><title>Automatic Loop-Shaping of QFT Controllers Using GAs and Evolutionary Computation</title><title>AI 2005: Advances in Artificial Intelligence</title><description>This paper presents a design method of the automatic loop-shaping which couples up manual loop-shaping method to genetic algorithms (GAs) in quantitative feedback theory (QFT). The loop-shaping is currently performed in computer aided design environments manually, and moreover, it is usually a trial and error procedure. To solve this problem, an automatic loop-shaping method based on GAs and evolutionary computation is developed and a benchmark example is used to examine the performance of the proposed automatic loop-shaping compared with that of the manual loop-shaping and similar other research.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Frequency Array</subject><subject>Margin Bound</subject><subject>Order Controller</subject><subject>Quantitative Feedback Theory</subject><subject>Tracking Bound</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540304622</isbn><isbn>9783540304623</isbn><isbn>3540316523</isbn><isbn>9783540316527</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkMtOwzAQRc1Loi3s-ABv2CAFZmwntpdV1RakSoBo15HjxiWQxlGcIPH3uGoXrOaOztVodAi5Q3hEAPmEmCqtNeQo5BkZ81QAxyxl_JyMMENMOBf64gRAZIxdklFMLNFS8GsyDuELAJjUbETepkPv96avLF153yYfn6atmh31jr4v1nTmm77zdV12gW7CASyngZpmS-c_vh76yjem-421fTv05rDekCtn6lDenuaEbBbz9ew5Wb0uX2bTVdIyBn0ijbNpobZaKOVKriRLswKZhEILazLn0CmFGS9AW8m3HFxWWmk0s046zoBPyP3xbmuCNbXrTGOrkLddtY8f5Si5SDFKmZCHYy9E1OzKLi-8_w45Rn_RZv7fJv8DJMxh9g</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Kim, Min-Soo</creator><creator>Chung, Chan-Soo</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>Automatic Loop-Shaping of QFT Controllers Using GAs and Evolutionary Computation</title><author>Kim, Min-Soo ; Chung, Chan-Soo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p220t-7afc5b8d9488fe387256b1270b94ca6ff1f88163b09c73d30f6ec7a92cf7f3203</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Frequency Array</topic><topic>Margin Bound</topic><topic>Order Controller</topic><topic>Quantitative Feedback Theory</topic><topic>Tracking Bound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kim, Min-Soo</creatorcontrib><creatorcontrib>Chung, Chan-Soo</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kim, Min-Soo</au><au>Chung, Chan-Soo</au><au>Jarvis, Ray</au><au>Zhang, Shichao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic Loop-Shaping of QFT Controllers Using GAs and Evolutionary Computation</atitle><btitle>AI 2005: Advances in Artificial Intelligence</btitle><date>2005</date><risdate>2005</risdate><spage>1096</spage><epage>1100</epage><pages>1096-1100</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540304622</isbn><isbn>9783540304623</isbn><eisbn>3540316523</eisbn><eisbn>9783540316527</eisbn><abstract>This paper presents a design method of the automatic loop-shaping which couples up manual loop-shaping method to genetic algorithms (GAs) in quantitative feedback theory (QFT). The loop-shaping is currently performed in computer aided design environments manually, and moreover, it is usually a trial and error procedure. To solve this problem, an automatic loop-shaping method based on GAs and evolutionary computation is developed and a benchmark example is used to examine the performance of the proposed automatic loop-shaping compared with that of the manual loop-shaping and similar other research.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11589990_147</doi><tpages>5</tpages></addata></record> |
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identifier | ISSN: 0302-9743 |
ispartof | AI 2005: Advances in Artificial Intelligence, 2005, p.1096-1100 |
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language | eng |
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source | Springer Books |
subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Frequency Array Margin Bound Order Controller Quantitative Feedback Theory Tracking Bound |
title | Automatic Loop-Shaping of QFT Controllers Using GAs and Evolutionary Computation |
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