A Neuro-augmented Observer for a Class of Nonlinear Systems
A new type of state observer for nonlinear systems is presented in this paper. This observer is a hybrid of linear and nonlinear parts: it is based on a conventional linear observer design, and augmented by a neural network. The neural network approximates only the nonlinear part of the system. The...
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| creator | Huajun Gong Hao Xu Chowdhury, F.N. |
| description | A new type of state observer for nonlinear systems is presented in this paper. This observer is a hybrid of linear and nonlinear parts: it is based on a conventional linear observer design, and augmented by a neural network. The neural network approximates only the nonlinear part of the system. The state estimation error is proved to approach zero asymptotically. |
| doi_str_mv | 10.1109/IJCNN.2006.247100 |
| format | Conference Proceeding |
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The state estimation error is proved to approach zero asymptotically.</description><subject>Convergence</subject><subject>Design methodology</subject><subject>Fault detection</subject><subject>NASA</subject><subject>Neural networks</subject><subject>Nonlinear dynamical systems</subject><subject>Nonlinear systems</subject><subject>Observers</subject><subject>State estimation</subject><subject>State feedback</subject><issn>2161-4393</issn><issn>2161-4407</issn><isbn>9780780394902</isbn><isbn>0780394909</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1jc1Kw0AURgd_wFr7AOJmXiDx3pnJJIOrEqxWSrpQ1-UmuSORNJGZVOjbW1Dhg7M4cD4hbhFSRHD365eyqlIFYFNlcgQ4EzOFFhNjID8XC5cXcJp2xoG6-Hfa6StxHeMngNLO6Zl4WMqKD2FM6PCx52HiVm7ryOGbg_RjkCTLnmKUo5fVOPTdwBTk6zFOvI834tJTH3nxx7l4Xz2-lc_JZvu0LpebpMM8m06vyK2nXJO1LZHNdGEbxegd1d5iA9T6RjsFtjbsVJv5hgvMsDa6IaWMnou7327HzLuv0O0pHHeYozUa9A_4FEl-</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Huajun Gong</creator><creator>Hao Xu</creator><creator>Chowdhury, F.N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2006</creationdate><title>A Neuro-augmented Observer for a Class of Nonlinear Systems</title><author>Huajun Gong ; Hao Xu ; Chowdhury, F.N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-431edfa73a66daa65386c2e1f9abf61c0adfc39206b4e92d5fce8151b43ca2243</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Convergence</topic><topic>Design methodology</topic><topic>Fault detection</topic><topic>NASA</topic><topic>Neural networks</topic><topic>Nonlinear dynamical systems</topic><topic>Nonlinear systems</topic><topic>Observers</topic><topic>State estimation</topic><topic>State feedback</topic><toplevel>online_resources</toplevel><creatorcontrib>Huajun Gong</creatorcontrib><creatorcontrib>Hao Xu</creatorcontrib><creatorcontrib>Chowdhury, F.N.</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>Huajun Gong</au><au>Hao Xu</au><au>Chowdhury, F.N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Neuro-augmented Observer for a Class of Nonlinear Systems</atitle><btitle>The 2006 IEEE International Joint Conference on Neural Network Proceedings</btitle><stitle>IJCNN</stitle><date>2006</date><risdate>2006</risdate><spage>2497</spage><epage>2500</epage><pages>2497-2500</pages><issn>2161-4393</issn><eissn>2161-4407</eissn><isbn>9780780394902</isbn><isbn>0780394909</isbn><abstract>A new type of state observer for nonlinear systems is presented in this paper. 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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Convergence Design methodology Fault detection NASA Neural networks Nonlinear dynamical systems Nonlinear systems Observers State estimation State feedback |
| title | A Neuro-augmented Observer for a Class of Nonlinear Systems |
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