Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems
In this study, a novel adaptive neural network (NN)-based leader-following consensus approach is proposed for a class of non-linear second-order multi-agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN-based adaptive consensus algorithms req...
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| Veröffentlicht in: | IET control theory & applications 2015-08, Vol.9 (13), p.1927-1934 |
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| container_issue | 13 |
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| container_title | IET control theory & applications |
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| creator | Wen, Guo-Xing Chen, C.L. Philip Liu, Yan-Jun Liu, Zhi |
| description | In this study, a novel adaptive neural network (NN)-based leader-following consensus approach is proposed for a class of non-linear second-order multi-agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN-based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. However, the proposed adaptive consensus scheme can greatly reduce the online computation burden, because the adaptive adjusting parameters are designed in scalar form, which is the norm of the estimation of the optimal NN weight matrix. According to Lyapunov stability theory, the proposed approach can guarantee the leader-following consensus behaviour of non-linear second-order multi-agent systems to be obtained. Finally, a numerical simulation and a multi-manipulator simulation are carried out to further demonstrate the effectiveness of the proposed consensus approach. |
| doi_str_mv | 10.1049/iet-cta.2014.1319 |
| format | Article |
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Philip</creatorcontrib><creatorcontrib>Liu, Yan-Jun</creatorcontrib><creatorcontrib>Liu, Zhi</creatorcontrib><title>Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems</title><title>IET control theory & applications</title><description>In this study, a novel adaptive neural network (NN)-based leader-following consensus approach is proposed for a class of non-linear second-order multi-agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN-based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. However, the proposed adaptive consensus scheme can greatly reduce the online computation burden, because the adaptive adjusting parameters are designed in scalar form, which is the norm of the estimation of the optimal NN weight matrix. According to Lyapunov stability theory, the proposed approach can guarantee the leader-following consensus behaviour of non-linear second-order multi-agent systems to be obtained. Finally, a numerical simulation and a multi-manipulator simulation are carried out to further demonstrate the effectiveness of the proposed consensus approach.</description><subject>adaptive control</subject><subject>adaptive leader following consensus control</subject><subject>Algorithms</subject><subject>Computation</subject><subject>Computer simulation</subject><subject>leader following consensus approach</subject><subject>Lyapunov methods</subject><subject>Lyapunov stability theory</subject><subject>Mathematical models</subject><subject>matrix algebra</subject><subject>Multiagent systems</subject><subject>multimanipulator simulation</subject><subject>multi‐agent systems</subject><subject>neural network</subject><subject>Neural networks</subject><subject>neurocontrollers</subject><subject>NN based adaptive consensus algorithms</subject><subject>NN nodes</subject><subject>nonlinear control systems</subject><subject>Nonlinearity</subject><subject>numerical analysis</subject><subject>numerical simulation</subject><subject>Online</subject><subject>online computation</subject><subject>optimal NN weight matrix</subject><subject>second order nonlinear multiagent systems</subject><issn>1751-8644</issn><issn>1751-8652</issn><issn>1751-8652</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNks1u3CAUha2okZImeYDsvGwXTMH8GLpLR_mTomYzWSMGXyKnDLiAO5q3j62Jqi6idFZcpO-ci8RXVZcELwhm6lsPBdliFg0mbEEoUUfVKWk5QVLw5tPfmbGT6nPOLxhzLhg_rYafMCbjUYCyjekXWpsMXW06M5T-D9QeTAcJueh93PbhubYxZAh5zPNUUvS1i6nOMN06FNME1yEG5PsAJtWb0ZcemWcIpc67XGCTz6tjZ3yGi7fzrHq6uV4t79DD4-398uoBWd4KgoBRa9uWWSzJWnJFbCMk5h0zjqw5FZIoKRUFxxhzzjjViYZiLg13mIFd07Pqy753SPH3CLnoTZ8teG8CxDFr0iraiEYocRjKJSXyQFRQyQ9EmVKHtjZYzm8le9SmmHMCp4fUb0zaaYL1bIKeTNCTCXo2Qc8mTJnv-8y297D7f0AvV3fNjxuMZUumMNqHZ-wljilMv_bhsq_v8PfXq6n16p8dQ-foK7RL1oM</recordid><startdate>20150827</startdate><enddate>20150827</enddate><creator>Wen, Guo-Xing</creator><creator>Chen, C.L. 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Philip ; Liu, Yan-Jun ; Liu, Zhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5761-e43cc774c081b8591c26805d4af1b5368198893ef444ffaf9d623058a5f04ecb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>adaptive control</topic><topic>adaptive leader following consensus control</topic><topic>Algorithms</topic><topic>Computation</topic><topic>Computer simulation</topic><topic>leader following consensus approach</topic><topic>Lyapunov methods</topic><topic>Lyapunov stability theory</topic><topic>Mathematical models</topic><topic>matrix algebra</topic><topic>Multiagent systems</topic><topic>multimanipulator simulation</topic><topic>multi‐agent systems</topic><topic>neural network</topic><topic>Neural networks</topic><topic>neurocontrollers</topic><topic>NN based adaptive consensus algorithms</topic><topic>NN nodes</topic><topic>nonlinear control systems</topic><topic>Nonlinearity</topic><topic>numerical analysis</topic><topic>numerical simulation</topic><topic>Online</topic><topic>online computation</topic><topic>optimal NN weight matrix</topic><topic>second order nonlinear multiagent systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wen, Guo-Xing</creatorcontrib><creatorcontrib>Chen, C.L. Philip</creatorcontrib><creatorcontrib>Liu, Yan-Jun</creatorcontrib><creatorcontrib>Liu, Zhi</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</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>IET control theory & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wen, Guo-Xing</au><au>Chen, C.L. Philip</au><au>Liu, Yan-Jun</au><au>Liu, Zhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems</atitle><jtitle>IET control theory & applications</jtitle><date>2015-08-27</date><risdate>2015</risdate><volume>9</volume><issue>13</issue><spage>1927</spage><epage>1934</epage><pages>1927-1934</pages><issn>1751-8644</issn><issn>1751-8652</issn><eissn>1751-8652</eissn><abstract>In this study, a novel adaptive neural network (NN)-based leader-following consensus approach is proposed for a class of non-linear second-order multi-agent systems. For the existing NN consensus approaches, to obtain the desired approximation accuracy, the NN-based adaptive consensus algorithms require the number of NN nodes to must be large enough, and thus the online computation burden often are very heavy. 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| subjects | adaptive control adaptive leader following consensus control Algorithms Computation Computer simulation leader following consensus approach Lyapunov methods Lyapunov stability theory Mathematical models matrix algebra Multiagent systems multimanipulator simulation multi‐agent systems neural network Neural networks neurocontrollers NN based adaptive consensus algorithms NN nodes nonlinear control systems Nonlinearity numerical analysis numerical simulation Online online computation optimal NN weight matrix second order nonlinear multiagent systems |
| title | Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems |
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