Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints
An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, ra...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2017-06, Vol.28 (6), p.1318-1330 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Ziting Chen Zhijun Li Chen, C. L. Philip |
| description | An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective. |
| doi_str_mv | 10.1109/TNNLS.2016.2538779 |
| format | Article |
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(IEEE) 2017</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-2b9526114ee811448f6effd1b78371eed24766d190b68b71cbc9549fc315b3dd3</citedby><cites>FETCH-LOGICAL-c351t-2b9526114ee811448f6effd1b78371eed24766d190b68b71cbc9549fc315b3dd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7435341$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7435341$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27008679$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ziting Chen</creatorcontrib><creatorcontrib>Zhijun Li</creatorcontrib><creatorcontrib>Chen, C. L. Philip</creatorcontrib><title>Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.</description><subject>Adaptive control</subject><subject>Barrier Lyapunov function (BLF)</subject><subject>Basis functions</subject><subject>Closed loop systems</subject><subject>Computer simulation</subject><subject>disturbance observer</subject><subject>Disturbance observers</subject><subject>Feedback control</subject><subject>Liapunov functions</subject><subject>MIMO</subject><subject>MIMO (control systems)</subject><subject>Neural networks</subject><subject>neural networks (NNs)</subject><subject>Nonlinear control</subject><subject>Nonlinear systems</subject><subject>Nonlinearity</subject><subject>Observers</subject><subject>Radial basis function</subject><subject>Robot arms</subject><subject>Robots</subject><subject>state/input saturation constraints</subject><subject>Uncertainty</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM1qGzEURkVpaUKaF0ihCLrpxq6upNHPMpikNTjOwgntIiA0M3fohLHGlTSBvH3l2vWidyEJdL6PyyHkCtgcgNmvD-v1ajPnDNScV8Jobd-Qcw6Kz7gw5u3prX-ekcuUnlkZxSol7XtyxjVjRml7Tp6uW7_L_QvSNU7RD3QxhhzHgY4dfQwNxuz7QO-Wd_d0PYahD-gj3bymjNtEf_T5F91kn5H60NJl2E15X5ByLKmcPpB3nR8SXh7vC_J4e_Ow-D5b3X9bLq5Xs0ZUkGe8thVXABLRlFOaTmHXtVBrIzQgtlxqpVqwrFam1tDUja2k7RoBVS3aVlyQL4feXRx_T5iy2_apwWHwAccpOTBcKcW11QX9_B_6PE4xlO0cBy3LcCkLxQ9UE8eUInZuF_utj68OmNvrd3_1u71-d9RfQp-O1VO9xfYU-Se7AB8PQI-Ip28tRSUkiD-5R4gR</recordid><startdate>20170601</startdate><enddate>20170601</enddate><creator>Ziting Chen</creator><creator>Zhijun Li</creator><creator>Chen, C. 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Philip</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-2b9526114ee811448f6effd1b78371eed24766d190b68b71cbc9549fc315b3dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive control</topic><topic>Barrier Lyapunov function (BLF)</topic><topic>Basis functions</topic><topic>Closed loop systems</topic><topic>Computer simulation</topic><topic>disturbance observer</topic><topic>Disturbance observers</topic><topic>Feedback control</topic><topic>Liapunov functions</topic><topic>MIMO</topic><topic>MIMO (control systems)</topic><topic>Neural networks</topic><topic>neural networks (NNs)</topic><topic>Nonlinear control</topic><topic>Nonlinear systems</topic><topic>Nonlinearity</topic><topic>Observers</topic><topic>Radial basis function</topic><topic>Robot arms</topic><topic>Robots</topic><topic>state/input saturation constraints</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Ziting Chen</creatorcontrib><creatorcontrib>Zhijun Li</creatorcontrib><creatorcontrib>Chen, C. 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L. Philip</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2017-06-01</date><risdate>2017</risdate><volume>28</volume><issue>6</issue><spage>1318</spage><epage>1330</epage><pages>1318-1330</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>27008679</pmid><doi>10.1109/TNNLS.2016.2538779</doi><tpages>13</tpages></addata></record> |
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| subjects | Adaptive control Barrier Lyapunov function (BLF) Basis functions Closed loop systems Computer simulation disturbance observer Disturbance observers Feedback control Liapunov functions MIMO MIMO (control systems) Neural networks neural networks (NNs) Nonlinear control Nonlinear systems Nonlinearity Observers Radial basis function Robot arms Robots state/input saturation constraints Uncertainty |
| title | Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints |
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