Small-scale helicopter system identification model using recurrent neural networks
Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs SeriesParallel (N...
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| creator | Taha, Zahari Deboucha, Abdelhakim Bin Dahari, Mahidzal |
| description | Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs SeriesParallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. This accuracy might be used to realize a reliable flight control for an autonomous helicopter. |
| doi_str_mv | 10.1109/TENCON.2010.5686070 |
| format | Conference Proceeding |
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This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs SeriesParallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. 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This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs SeriesParallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. This accuracy might be used to realize a reliable flight control for an autonomous helicopter.</description><subject>Artificial neural networks</subject><subject>Dynamics model</subject><subject>Helicopters</subject><subject>Mathematical model</subject><subject>Nonlinear dynamical systems</subject><subject>Recurrent Neural Network (RNN)</subject><subject>Recurrent neural networks</subject><subject>Small-Scale Helicopter</subject><subject>System identification</subject><subject>Vehicle dynamics</subject><issn>2159-3442</issn><issn>2159-3450</issn><isbn>9781424468898</isbn><isbn>1424468892</isbn><isbn>1424468884</isbn><isbn>9781424468881</isbn><isbn>1424468906</isbn><isbn>9781424468904</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAURM1LopR-QTf-gRTbuXaulygqD6lqJSjrynEcMDhJZadC_XsiUZjNjOZIsxhC5pwtOGf6brtcl5v1QrCxkAoVK9gZueEgABQiwjmZCC51loNkF2SmC_xjGi__GYhrMkvpk41STDAsJuTltTUhZMma4OiHC972-8FFmo5pcC31tesG33hrBt93tO1rF-gh-e6dRmcPMY6Ydu4QTRht-O7jV7olV40Jyc1OPiVvD8tt-ZStNo_P5f0q87yQQ4YCpagrK5StBJOoxsx0pdA0qoHcItpG1ABQN4W0eQVWK1CFMc7kGrjMp2T-u-udc7t99K2Jx93pnvwHTiFW1w</recordid><startdate>201011</startdate><enddate>201011</enddate><creator>Taha, Zahari</creator><creator>Deboucha, Abdelhakim</creator><creator>Bin Dahari, Mahidzal</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201011</creationdate><title>Small-scale helicopter system identification model using recurrent neural networks</title><author>Taha, Zahari ; Deboucha, Abdelhakim ; Bin Dahari, Mahidzal</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-82852dbc26cb20586dbc09b68af6f43c88cf2d444df75c3b4c96467aaea394153</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>Dynamics model</topic><topic>Helicopters</topic><topic>Mathematical model</topic><topic>Nonlinear dynamical systems</topic><topic>Recurrent Neural Network (RNN)</topic><topic>Recurrent neural networks</topic><topic>Small-Scale Helicopter</topic><topic>System identification</topic><topic>Vehicle dynamics</topic><toplevel>online_resources</toplevel><creatorcontrib>Taha, Zahari</creatorcontrib><creatorcontrib>Deboucha, Abdelhakim</creatorcontrib><creatorcontrib>Bin Dahari, Mahidzal</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Taha, Zahari</au><au>Deboucha, Abdelhakim</au><au>Bin Dahari, Mahidzal</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Small-scale helicopter system identification model using recurrent neural networks</atitle><btitle>TENCON 2010 - 2010 IEEE Region 10 Conference</btitle><stitle>TENCON</stitle><date>2010-11</date><risdate>2010</risdate><spage>1393</spage><epage>1397</epage><pages>1393-1397</pages><issn>2159-3442</issn><eissn>2159-3450</eissn><isbn>9781424468898</isbn><isbn>1424468892</isbn><eisbn>1424468884</eisbn><eisbn>9781424468881</eisbn><eisbn>1424468906</eisbn><eisbn>9781424468904</eisbn><abstract>Designing a reliable flight control for an autonomous helicopter requires a high performance dynamics model. This paper studies the recurrent neural network nonlinear model identification of a small scale helicopter. We have selected a Nonlinear AutoRegressive with eXogenous Inputs SeriesParallel (NARXSP) network model which identifies the dynamics model of an unmanned aerial helicopter from real flight data. The identification process is conducted by using the well known Levenberg-Marquardt learning algorithm. The obtained dynamics model shows good fitness with the actual data. This accuracy might be used to realize a reliable flight control for an autonomous helicopter.</abstract><pub>IEEE</pub><doi>10.1109/TENCON.2010.5686070</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Dynamics model Helicopters Mathematical model Nonlinear dynamical systems Recurrent Neural Network (RNN) Recurrent neural networks Small-Scale Helicopter System identification Vehicle dynamics |
| title | Small-scale helicopter system identification model using recurrent neural networks |
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