Robust backstepping control of nonlinear systems using neural networks
A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not requi...
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| Veröffentlicht in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2000-11, Vol.30 (6), p.753-766 |
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| container_title | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans |
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| creator | Kwan, C. Lewis, F.L. |
| description | A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme. |
| doi_str_mv | 10.1109/3468.895898 |
| format | Article |
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A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. 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Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme.</description><subject>Adaptive control</subject><subject>Adaptive control systems</subject><subject>Backstepping</subject><subject>Control systems</subject><subject>Dynamical systems</subject><subject>Error correction</subject><subject>Neural networks</subject><subject>Nonlinear control systems</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Programmable control</subject><subject>Regression</subject><subject>Robots</subject><subject>Robust control</subject><subject>Tuning</subject><issn>1083-4427</issn><issn>2168-2216</issn><issn>1558-2426</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0cFLwzAUBvAgCs7pyZun4kEP0pmkSZocZTgVBoLoOaTtq3Trkpm0yP57Uzo8eHCnF3g_vpB8CF0SPCMEq_uMCTmTikslj9CEcC5Tyqg4jmcss5Qxmp-isxBWGBPGFJugxZsr-tAlhSnXoYPttrGfSels512buDqxzraNBeOTsIv7TUj6MBALvTdtHN238-twjk5q0wa42M8p-lg8vs-f0-Xr08v8YZmWDNMuJaLOCSU15VUBLDOYVlwZKnIONRSkkLIUFSNYVDQ-p6iySuUZxiUFaQrgOJui2zF3691XD6HTmyaU0LbGguuDVoSJjAgqorz5V1KF4x2YHYaSC45zdRjmQjKuhsTrP3Dlem_jv2gpYxRRjEZ0N6LSuxA81Hrrm43xO02wHsrUQ5l6LDPqq1E3APAr98sfO3iY0A</recordid><startdate>20001101</startdate><enddate>20001101</enddate><creator>Kwan, C.</creator><creator>Lewis, F.L.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20001101</creationdate><title>Robust backstepping control of nonlinear systems using neural networks</title><author>Kwan, C. ; Lewis, F.L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-16f7121f25dbe43a02d59a2675efeb1b88c6d4106d2109bd3d97300c2e8abe503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Adaptive control</topic><topic>Adaptive control systems</topic><topic>Backstepping</topic><topic>Control systems</topic><topic>Dynamical systems</topic><topic>Error correction</topic><topic>Neural networks</topic><topic>Nonlinear control systems</topic><topic>Nonlinear dynamics</topic><topic>Nonlinear systems</topic><topic>Programmable control</topic><topic>Regression</topic><topic>Robots</topic><topic>Robust control</topic><topic>Tuning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kwan, C.</creatorcontrib><creatorcontrib>Lewis, F.L.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kwan, C.</au><au>Lewis, F.L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Robust backstepping control of nonlinear systems using neural networks</atitle><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle><stitle>TSMCA</stitle><date>2000-11-01</date><risdate>2000</risdate><volume>30</volume><issue>6</issue><spage>753</spage><epage>766</epage><pages>753-766</pages><issn>1083-4427</issn><issn>2168-2216</issn><eissn>1558-2426</eissn><eissn>2168-2232</eissn><coden>ITSHFX</coden><abstract>A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. 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| ispartof | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans, 2000-11, Vol.30 (6), p.753-766 |
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| language | eng |
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| source | IEEE Xplore |
| subjects | Adaptive control Adaptive control systems Backstepping Control systems Dynamical systems Error correction Neural networks Nonlinear control systems Nonlinear dynamics Nonlinear systems Programmable control Regression Robots Robust control Tuning |
| title | Robust backstepping control of nonlinear systems using neural networks |
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