Strategies for genetic adaptive control
In this paper, we investigate ways to use genetic algorithms in the online control of a nonlinear system and compare our results with conventional control techniques. We develop a direct genetic adaptive controller, an indirect genetic adaptive controller, and combine the two into a general genetic...
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| creator | Lennon, W.K. Passino, K.M. |
| description | In this paper, we investigate ways to use genetic algorithms in the online control of a nonlinear system and compare our results with conventional control techniques. We develop a direct genetic adaptive controller, an indirect genetic adaptive controller, and combine the two into a general genetic adaptive controller. We also examine several conventional controllers including a proportional-derivative (PD) controller, a model reference adaptive controller, and two indirect adaptive controllers. To demonstrate all these control techniques, we investigate the problem of cargo ship steering. In this application, we describe the desired performance with a reference model and use our control techniques to track the output of the reference model. Overall, our goal is not to design the best possible controller for ship steering; we simply use this example to illustrate the ideas. |
| doi_str_mv | 10.1109/CDC.1997.657869 |
| format | Conference Proceeding |
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Overall, our goal is not to design the best possible controller for ship steering; we simply use this example to illustrate the ideas.</description><identifier>ISSN: 0191-2216</identifier><identifier>ISBN: 0780341872</identifier><identifier>ISBN: 9780780341876</identifier><identifier>DOI: 10.1109/CDC.1997.657869</identifier><language>eng</language><publisher>IEEE</publisher><subject>Adaptive control ; Algorithm design and analysis ; Control systems ; Genetic algorithms ; Integrated circuit modeling ; Marine vehicles ; Optimal control ; PD control ; Programmable control ; Proportional control</subject><ispartof>Proceedings of the 36th IEEE Conference on Decision and Control, 1997, Vol.2, p.1908-1913 vol.2</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/657869$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/657869$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lennon, W.K.</creatorcontrib><creatorcontrib>Passino, K.M.</creatorcontrib><title>Strategies for genetic adaptive control</title><title>Proceedings of the 36th IEEE Conference on Decision and Control</title><addtitle>CDC</addtitle><description>In this paper, we investigate ways to use genetic algorithms in the online control of a nonlinear system and compare our results with conventional control techniques. 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Overall, our goal is not to design the best possible controller for ship steering; we simply use this example to illustrate the ideas.</description><subject>Adaptive control</subject><subject>Algorithm design and analysis</subject><subject>Control systems</subject><subject>Genetic algorithms</subject><subject>Integrated circuit modeling</subject><subject>Marine vehicles</subject><subject>Optimal control</subject><subject>PD control</subject><subject>Programmable control</subject><subject>Proportional control</subject><issn>0191-2216</issn><isbn>0780341872</isbn><isbn>9780780341876</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1997</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotz79LAzEUwPGACrbVWXC6zenO95JLcm-U06pQcFDnkpcfJVJ7JRcE_3uHOn23D3yFuEHoEIHux8exQyLbGW0HQ2diCXYA1eNg5blYABK2UqK5FMt5_gKAAYxZiLv3WlyNuxznJk2l2cVDrNk3LrhjzT-x8dOhlml_JS6S28_x-r8r8bl--hhf2s3b8-v4sGkzWllbjRaZQSrtUAdvOFgOxOQB2JuBnYbAGlyyiIkS94YAgUmz76VRSq3E7cnNMcbtseRvV363pyf1B7DyPyg</recordid><startdate>1997</startdate><enddate>1997</enddate><creator>Lennon, W.K.</creator><creator>Passino, K.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1997</creationdate><title>Strategies for genetic adaptive control</title><author>Lennon, W.K. ; Passino, K.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-5171bb0235a15dc6bd7bd9b9c00bc68ba50db50af711f9fb469010b95bc426333</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Adaptive control</topic><topic>Algorithm design and analysis</topic><topic>Control systems</topic><topic>Genetic algorithms</topic><topic>Integrated circuit modeling</topic><topic>Marine vehicles</topic><topic>Optimal control</topic><topic>PD control</topic><topic>Programmable control</topic><topic>Proportional control</topic><toplevel>online_resources</toplevel><creatorcontrib>Lennon, W.K.</creatorcontrib><creatorcontrib>Passino, K.M.</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>Lennon, W.K.</au><au>Passino, K.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Strategies for genetic adaptive control</atitle><btitle>Proceedings of the 36th IEEE Conference on Decision and Control</btitle><stitle>CDC</stitle><date>1997</date><risdate>1997</risdate><volume>2</volume><spage>1908</spage><epage>1913 vol.2</epage><pages>1908-1913 vol.2</pages><issn>0191-2216</issn><isbn>0780341872</isbn><isbn>9780780341876</isbn><abstract>In this paper, we investigate ways to use genetic algorithms in the online control of a nonlinear system and compare our results with conventional control techniques. We develop a direct genetic adaptive controller, an indirect genetic adaptive controller, and combine the two into a general genetic adaptive controller. We also examine several conventional controllers including a proportional-derivative (PD) controller, a model reference adaptive controller, and two indirect adaptive controllers. To demonstrate all these control techniques, we investigate the problem of cargo ship steering. In this application, we describe the desired performance with a reference model and use our control techniques to track the output of the reference model. Overall, our goal is not to design the best possible controller for ship steering; we simply use this example to illustrate the ideas.</abstract><pub>IEEE</pub><doi>10.1109/CDC.1997.657869</doi></addata></record> |
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| ispartof | Proceedings of the 36th IEEE Conference on Decision and Control, 1997, Vol.2, p.1908-1913 vol.2 |
| issn | 0191-2216 |
| language | eng |
| recordid | cdi_ieee_primary_657869 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Adaptive control Algorithm design and analysis Control systems Genetic algorithms Integrated circuit modeling Marine vehicles Optimal control PD control Programmable control Proportional control |
| title | Strategies for genetic adaptive control |
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