Neural network-based sliding mode adaptive control for robot manipulators
This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptiv...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) 2011-07, Vol.74 (14), p.2377-2384 |
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| container_title | Neurocomputing (Amsterdam) |
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| creator | Sun, Tairen Pei, Hailong Pan, Yongping Zhou, Hongbo Zhang, Caihong |
| description | This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations. |
| doi_str_mv | 10.1016/j.neucom.2011.03.015 |
| format | Article |
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First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.</description><identifier>ISSN: 0925-2312</identifier><identifier>EISSN: 1872-8286</identifier><identifier>DOI: 10.1016/j.neucom.2011.03.015</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Asymptotic properties ; Manipulators ; Neural network (NN) ; Neural networks ; Observers ; Output feedback control ; Robot arms ; Robot manipulators ; Robots ; Sliding mode ; Sliding mode adaptive control ; Trajectories</subject><ispartof>Neurocomputing (Amsterdam), 2011-07, Vol.74 (14), p.2377-2384</ispartof><rights>2011 Elsevier B.V.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c339t-7d6e1360deb6ec955a3c1e56f05835b1cfbb3341c33cc836110bcf81034177f33</citedby><cites>FETCH-LOGICAL-c339t-7d6e1360deb6ec955a3c1e56f05835b1cfbb3341c33cc836110bcf81034177f33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0925231211002141$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids></links><search><creatorcontrib>Sun, Tairen</creatorcontrib><creatorcontrib>Pei, Hailong</creatorcontrib><creatorcontrib>Pan, Yongping</creatorcontrib><creatorcontrib>Zhou, Hongbo</creatorcontrib><creatorcontrib>Zhang, Caihong</creatorcontrib><title>Neural network-based sliding mode adaptive control for robot manipulators</title><title>Neurocomputing (Amsterdam)</title><description>This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.</description><subject>Asymptotic properties</subject><subject>Manipulators</subject><subject>Neural network (NN)</subject><subject>Neural networks</subject><subject>Observers</subject><subject>Output feedback control</subject><subject>Robot arms</subject><subject>Robot manipulators</subject><subject>Robots</subject><subject>Sliding mode</subject><subject>Sliding mode adaptive control</subject><subject>Trajectories</subject><issn>0925-2312</issn><issn>1872-8286</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAURYMoOI7-AxddumnNS9q03Qgy-DEw6EbXIU1eJWPb1CQd8d_boa5dPbice-EdQq6BZkBB3O6zASft-oxRgIzyjEJxQlZQlSytWCVOyYrWrEgZB3ZOLkLYUwolsHpFti84edUlA8Zv5z_TRgU0SeisscNH0juDiTJqjPaAiXZD9K5LWucT7xoXk14Ndpw6FZ0Pl-SsVV3Aq7-7Ju-PD2-b53T3-rTd3O9SzXkd09IIBC6owUagrotCcQ1YiJYWFS8a0G3TcJ7DTGtdcQFAG91WQOesLFvO1-Rm2R29-5owRNnboLHr1IBuChJECTmvypzNaL6g2rsQPLZy9LZX_kcClUdzci8Xc_JoTlIuZ3Nz7W6p4fzGwaKXQVscNBrrUUdpnP1_4Bdyd3l6</recordid><startdate>20110701</startdate><enddate>20110701</enddate><creator>Sun, Tairen</creator><creator>Pei, Hailong</creator><creator>Pan, Yongping</creator><creator>Zhou, Hongbo</creator><creator>Zhang, Caihong</creator><general>Elsevier B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110701</creationdate><title>Neural network-based sliding mode adaptive control for robot manipulators</title><author>Sun, Tairen ; Pei, Hailong ; Pan, Yongping ; Zhou, Hongbo ; Zhang, Caihong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c339t-7d6e1360deb6ec955a3c1e56f05835b1cfbb3341c33cc836110bcf81034177f33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Asymptotic properties</topic><topic>Manipulators</topic><topic>Neural network (NN)</topic><topic>Neural networks</topic><topic>Observers</topic><topic>Output feedback control</topic><topic>Robot arms</topic><topic>Robot manipulators</topic><topic>Robots</topic><topic>Sliding mode</topic><topic>Sliding mode adaptive control</topic><topic>Trajectories</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Tairen</creatorcontrib><creatorcontrib>Pei, Hailong</creatorcontrib><creatorcontrib>Pan, Yongping</creatorcontrib><creatorcontrib>Zhou, Hongbo</creatorcontrib><creatorcontrib>Zhang, Caihong</creatorcontrib><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>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><jtitle>Neurocomputing (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Tairen</au><au>Pei, Hailong</au><au>Pan, Yongping</au><au>Zhou, Hongbo</au><au>Zhang, Caihong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural network-based sliding mode adaptive control for robot manipulators</atitle><jtitle>Neurocomputing (Amsterdam)</jtitle><date>2011-07-01</date><risdate>2011</risdate><volume>74</volume><issue>14</issue><spage>2377</spage><epage>2384</epage><pages>2377-2384</pages><issn>0925-2312</issn><eissn>1872-8286</eissn><abstract>This paper addresses the robust trajectory tracking problem for a robot manipulator in the presence of uncertainties and disturbances. First, a neural network-based sliding mode adaptive control (NNSMAC), which is a combination of sliding mode technique, neural network (NN) approximation and adaptive technique, is designed to ensure trajectory tracking by the robot manipulator. It is shown using the Lyapunov theory that the tracking error asymptotically converge to zero. However, the assumption on the availability of the robot manipulator dynamics is not always practical. So, an NN-based adaptive observer is designed to estimate the velocities of the links. Next, based on the observer, a neural network-based sliding mode adaptive output feedback control (NNSMAOFC) is designed. Then it is shown by the Lyapunov theory that the trajectory tracking errors, the observer estimation errors asymptotically converge to zero. The effectiveness of the designed NNSMAC, the NN-based adaptive observer and the NNSMAOFC is illustrated by simulations.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.neucom.2011.03.015</doi><tpages>8</tpages></addata></record> |
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| source | Elsevier ScienceDirect Journals |
| subjects | Asymptotic properties Manipulators Neural network (NN) Neural networks Observers Output feedback control Robot arms Robot manipulators Robots Sliding mode Sliding mode adaptive control Trajectories |
| title | Neural network-based sliding mode adaptive control for robot manipulators |
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