Adaptive neuro-fuzzy controllers for an open-loop morphing wing system
Abstract A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoi...
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| Veröffentlicht in: | Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering Journal of aerospace engineering, 2009-11, Vol.223 (7), p.965-975 |
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| container_title | Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering |
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| creator | Grigorie, T L Botez, R M Popov, A V |
| description | Abstract
A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoil deformations produced by the actuators' system. The pressures are calculated at different chord positions and will also be measured during wind tunnel tests. During a first identification phase, the two fuzzy inference systems (FISs) from the controllers' structure are generated for 16 flight conditions characterized by Mach numbers and angles of attack. Next, the FIS are optimized with the Matlab function adaptive neuro-fuzzy inference system (ANFIS) by training over different epochs. Finally, the controllers are validated for the other 33 flight conditions of the open-loop morphing wing system. This is the first time that such a method of relating the pressure differences to airfoil displacements has been conceived and used in an open-loop morphing wing controller system. |
| doi_str_mv | 10.1243/09544100JAERO487 |
| format | Article |
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A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoil deformations produced by the actuators' system. The pressures are calculated at different chord positions and will also be measured during wind tunnel tests. During a first identification phase, the two fuzzy inference systems (FISs) from the controllers' structure are generated for 16 flight conditions characterized by Mach numbers and angles of attack. Next, the FIS are optimized with the Matlab function adaptive neuro-fuzzy inference system (ANFIS) by training over different epochs. Finally, the controllers are validated for the other 33 flight conditions of the open-loop morphing wing system. This is the first time that such a method of relating the pressure differences to airfoil displacements has been conceived and used in an open-loop morphing wing controller system.</description><identifier>ISSN: 0954-4100</identifier><identifier>EISSN: 2041-3025</identifier><identifier>DOI: 10.1243/09544100JAERO487</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Actuators ; Adaptive systems ; Aircraft ; Airfoils ; Angle of attack ; Artificial neural networks ; Control systems ; Controllers ; Correlation ; Deformation ; Design engineering ; Displacement ; Flight conditions ; Fuzzy control ; Fuzzy logic ; Fuzzy sets ; Fuzzy systems ; Inference ; Mathematical analysis ; Matlab ; Morphing ; Simulation ; Systems design ; Training ; Wind tunnel testing ; Wing design ; Wings (aircraft)</subject><ispartof>Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering, 2009-11, Vol.223 (7), p.965-975</ispartof><rights>2009 Institution of Mechanical Engineers</rights><rights>Copyright Professional Engineering Publishing Ltd Nov 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-868ea41046caed7364afdaf28daa47ab4a5cae7f37e660ffaf0abb50bf24fa13</citedby><cites>FETCH-LOGICAL-c369t-868ea41046caed7364afdaf28daa47ab4a5cae7f37e660ffaf0abb50bf24fa13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://journals.sagepub.com/doi/pdf/10.1243/09544100JAERO487$$EPDF$$P50$$Gsage$$H</linktopdf><linktohtml>$$Uhttps://journals.sagepub.com/doi/10.1243/09544100JAERO487$$EHTML$$P50$$Gsage$$H</linktohtml><link.rule.ids>314,780,784,21819,27924,27925,43621,43622</link.rule.ids></links><search><creatorcontrib>Grigorie, T L</creatorcontrib><creatorcontrib>Botez, R M</creatorcontrib><creatorcontrib>Popov, A V</creatorcontrib><title>Adaptive neuro-fuzzy controllers for an open-loop morphing wing system</title><title>Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering</title><description>Abstract
A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoil deformations produced by the actuators' system. The pressures are calculated at different chord positions and will also be measured during wind tunnel tests. During a first identification phase, the two fuzzy inference systems (FISs) from the controllers' structure are generated for 16 flight conditions characterized by Mach numbers and angles of attack. Next, the FIS are optimized with the Matlab function adaptive neuro-fuzzy inference system (ANFIS) by training over different epochs. Finally, the controllers are validated for the other 33 flight conditions of the open-loop morphing wing system. This is the first time that such a method of relating the pressure differences to airfoil displacements has been conceived and used in an open-loop morphing wing controller system.</description><subject>Actuators</subject><subject>Adaptive systems</subject><subject>Aircraft</subject><subject>Airfoils</subject><subject>Angle of attack</subject><subject>Artificial neural networks</subject><subject>Control systems</subject><subject>Controllers</subject><subject>Correlation</subject><subject>Deformation</subject><subject>Design engineering</subject><subject>Displacement</subject><subject>Flight conditions</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Inference</subject><subject>Mathematical analysis</subject><subject>Matlab</subject><subject>Morphing</subject><subject>Simulation</subject><subject>Systems design</subject><subject>Training</subject><subject>Wind tunnel testing</subject><subject>Wing design</subject><subject>Wings (aircraft)</subject><issn>0954-4100</issn><issn>2041-3025</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kL1rwzAQxUVpoenH3tG0Sxe1J0uW5TGEpB8EAiW7OdtS6mBbrmS3JH99bdKhBHLD3fB-93g8Qu4YPLFQ8GdIIiEYwPt0_rESKj4jkxAEoxzC6JxMRpmO-iW58n4Lw0SST8hiWmDbld86aHTvLDX9fr8Lctt0zlaVdj4w1gXYBLbVDa2sbYPauvazbDbBz7j8zne6viEXBiuvb__uNVkv5uvZK12uXt5m0yXNuUw6qqTSOKQQMkddxFwKNAWaUBWIIsZMYDQIseGxlhKMQQOYZRFkJhQGGb8mjwfb1tmvXvsurUuf66rCRtvep0zGjCcqjJMBvT9Ct7Z3zRAuDRlnSig5-j2cglgCCTAFkRgoOFC5s947bdLWlTW6XcogHctPj8sfXujhxeNG_zM9xf8CJiSFDg</recordid><startdate>20091101</startdate><enddate>20091101</enddate><creator>Grigorie, T L</creator><creator>Botez, R M</creator><creator>Popov, A V</creator><general>SAGE Publications</general><general>SAGE PUBLICATIONS, INC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope><scope>3V.</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>L6V</scope><scope>M1Q</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20091101</creationdate><title>Adaptive neuro-fuzzy controllers for an open-loop morphing wing system</title><author>Grigorie, T L ; Botez, R M ; Popov, A V</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c369t-868ea41046caed7364afdaf28daa47ab4a5cae7f37e660ffaf0abb50bf24fa13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Actuators</topic><topic>Adaptive systems</topic><topic>Aircraft</topic><topic>Airfoils</topic><topic>Angle of attack</topic><topic>Artificial neural networks</topic><topic>Control systems</topic><topic>Controllers</topic><topic>Correlation</topic><topic>Deformation</topic><topic>Design engineering</topic><topic>Displacement</topic><topic>Flight conditions</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Inference</topic><topic>Mathematical analysis</topic><topic>Matlab</topic><topic>Morphing</topic><topic>Simulation</topic><topic>Systems design</topic><topic>Training</topic><topic>Wind tunnel testing</topic><topic>Wing design</topic><topic>Wings (aircraft)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Grigorie, T L</creatorcontrib><creatorcontrib>Botez, R M</creatorcontrib><creatorcontrib>Popov, A V</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Military Database</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Grigorie, T L</au><au>Botez, R M</au><au>Popov, A V</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive neuro-fuzzy controllers for an open-loop morphing wing system</atitle><jtitle>Proceedings of the Institution of Mechanical Engineers. Part G, Journal of aerospace engineering</jtitle><date>2009-11-01</date><risdate>2009</risdate><volume>223</volume><issue>7</issue><spage>965</spage><epage>975</epage><pages>965-975</pages><issn>0954-4100</issn><eissn>2041-3025</eissn><abstract>Abstract
A new method for the realization of two neuro-fuzzy controllers for a morphing wing design application is presented here. The controllers' main function is to correlate each set of pressure differences, calculated between the optimized and the reference airfoil, with each of the airfoil deformations produced by the actuators' system. The pressures are calculated at different chord positions and will also be measured during wind tunnel tests. During a first identification phase, the two fuzzy inference systems (FISs) from the controllers' structure are generated for 16 flight conditions characterized by Mach numbers and angles of attack. Next, the FIS are optimized with the Matlab function adaptive neuro-fuzzy inference system (ANFIS) by training over different epochs. Finally, the controllers are validated for the other 33 flight conditions of the open-loop morphing wing system. This is the first time that such a method of relating the pressure differences to airfoil displacements has been conceived and used in an open-loop morphing wing controller system.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><doi>10.1243/09544100JAERO487</doi><tpages>11</tpages></addata></record> |
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| subjects | Actuators Adaptive systems Aircraft Airfoils Angle of attack Artificial neural networks Control systems Controllers Correlation Deformation Design engineering Displacement Flight conditions Fuzzy control Fuzzy logic Fuzzy sets Fuzzy systems Inference Mathematical analysis Matlab Morphing Simulation Systems design Training Wind tunnel testing Wing design Wings (aircraft) |
| title | Adaptive neuro-fuzzy controllers for an open-loop morphing wing system |
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