Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs
This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of {N} subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the stud...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2016-01, Vol.46 (1), p.9-19 |
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| creator | Yan-Jun Liu Shaocheng Tong Chen, C. L. Philip Dong-Juan Li |
| description | This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of {N} subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl-Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. The closed-loop system can be proven to be stable by using Lyapunov theorem. A simulation example is studied to validate the effectiveness of the scheme. |
| doi_str_mv | 10.1109/TCYB.2015.2388582 |
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L. Philip ; Dong-Juan Li</creator><creatorcontrib>Yan-Jun Liu ; Shaocheng Tong ; Chen, C. L. Philip ; Dong-Juan Li</creatorcontrib><description>This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of {N} subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl-Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. 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(IEEE) 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c485t-6ada1d7bb396e1d33b9ab3f8d42105178f972c371206c0d764ad7a40a42df4853</citedby><cites>FETCH-LOGICAL-c485t-6ada1d7bb396e1d33b9ab3f8d42105178f972c371206c0d764ad7a40a42df4853</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7087385$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7087385$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25898325$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yan-Jun Liu</creatorcontrib><creatorcontrib>Shaocheng Tong</creatorcontrib><creatorcontrib>Chen, C. L. Philip</creatorcontrib><creatorcontrib>Dong-Juan Li</creatorcontrib><title>Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs</title><title>IEEE transactions on cybernetics</title><addtitle>TCYB</addtitle><addtitle>IEEE Trans Cybern</addtitle><description>This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of {N} subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl-Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. The closed-loop system can be proven to be stable by using Lyapunov theorem. A simulation example is studied to validate the effectiveness of the scheme.</description><subject>Adaptation models</subject><subject>Adaptive control</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Computer Simulation</subject><subject>Control systems</subject><subject>Controllers</subject><subject>Hysteresis</subject><subject>intelligent control</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>MIMO</subject><subject>MIMO (control systems)</subject><subject>Models, Theoretical</subject><subject>Neural networks</subject><subject>Neural Networks (Computer)</subject><subject>neural networks (NNs)</subject><subject>nonlinear control theory</subject><subject>Nonlinear Dynamics</subject><subject>Nonlinearity</subject><subject>Prandtl-Ishlinskii (PI) hysteresis inputs</subject><issn>2168-2267</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqNkUtP3DAURq2qVUGUH1BVqix1002mfttZwpTHSDwWgBCryIlv2tCMPdgJiH-P0xlm0U3x5lrX536WfRD6TMmMUlL-uJ7fHc4YoXLGuDHSsHdol1FlCsa0fL_dK72D9lO6J3mZ3CrNR7TDZK6cyV3UXcAYbY_nwQ8x9D1E_BNS98sXhzaBwwfOrobuEV4B3IaIL4LvOw824vPF-SW-ek4DLBO-7Ybf-Mb_8eHJ49OpGXNUwgu_Gof0CX1obZ9gf1P30M3x0fX8tDi7PFnMD86KRhg5FMo6S52ua14qoI7zurQ1b40TjBJJtWlLzRquKSOqIU4rYZ22gljBXJsT-B76vs5dxfAwQhqqZZca6HvrIYypooZrzojk9P-oNorRUgn9BlQSwRUXLKPf_kHvwxh9fvNfiphSyOluuqaaGFKK0Far2C1tfK4oqSbB1SS4mgRXG8F55usmeayX4LYTrzoz8GUNdACwPdbEaJ5_5gULRafG</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Yan-Jun Liu</creator><creator>Shaocheng Tong</creator><creator>Chen, C. L. Philip</creator><creator>Dong-Juan Li</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7X8</scope><scope>7TK</scope></search><sort><creationdate>20160101</creationdate><title>Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs</title><author>Yan-Jun Liu ; Shaocheng Tong ; Chen, C. L. Philip ; Dong-Juan Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c485t-6ada1d7bb396e1d33b9ab3f8d42105178f972c371206c0d764ad7a40a42df4853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adaptation models</topic><topic>Adaptive control</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Computer Simulation</topic><topic>Control systems</topic><topic>Controllers</topic><topic>Hysteresis</topic><topic>intelligent control</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>MIMO</topic><topic>MIMO (control systems)</topic><topic>Models, Theoretical</topic><topic>Neural networks</topic><topic>Neural Networks (Computer)</topic><topic>neural networks (NNs)</topic><topic>nonlinear control theory</topic><topic>Nonlinear Dynamics</topic><topic>Nonlinearity</topic><topic>Prandtl-Ishlinskii (PI) hysteresis inputs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yan-Jun Liu</creatorcontrib><creatorcontrib>Shaocheng Tong</creatorcontrib><creatorcontrib>Chen, C. 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L. Philip</au><au>Dong-Juan Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TCYB</stitle><addtitle>IEEE Trans Cybern</addtitle><date>2016-01-01</date><risdate>2016</risdate><volume>46</volume><issue>1</issue><spage>9</spage><epage>19</epage><pages>9-19</pages><issn>2168-2267</issn><eissn>2168-2275</eissn><coden>ITCEB8</coden><abstract>This paper studies an adaptive neural control for nonlinear multiple-input multiple-output systems in interconnected form. The studied systems are composed of {N} subsystems in pure feedback structure and the interconnection terms are contained in every equation of each subsystem. Moreover, the studied systems consider the effects of Prandtl-Ishlinskii (PI) hysteresis model. It is for the first time to study the control problem for such a class of systems. In addition, the proposed scheme removes an important assumption imposed on the previous works that the bounds of the parameters in PI hysteresis are known. The radial basis functions neural networks are employed to approximate unknown functions. The adaptation laws and the controllers are designed by employing the backstepping technique. The closed-loop system can be proven to be stable by using Lyapunov theorem. A simulation example is studied to validate the effectiveness of the scheme.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>25898325</pmid><doi>10.1109/TCYB.2015.2388582</doi><tpages>11</tpages></addata></record> |
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| subjects | Adaptation models Adaptive control Algorithms Artificial neural networks Computer Simulation Control systems Controllers Hysteresis intelligent control Mathematical model Mathematical models MIMO MIMO (control systems) Models, Theoretical Neural networks Neural Networks (Computer) neural networks (NNs) nonlinear control theory Nonlinear Dynamics Nonlinearity Prandtl-Ishlinskii (PI) hysteresis inputs |
| title | Neural Controller Design-Based Adaptive Control for Nonlinear MIMO Systems With Unknown Hysteresis Inputs |
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