Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics
This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are comb...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2018-08, Vol.29 (8), p.3658-3668 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Wang, Huanqing Liu, Peter Xiaoping Li, Shuai Wang, Ding |
| description | This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results. |
| doi_str_mv | 10.1109/TNNLS.2017.2716947 |
| format | Article |
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An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2017.2716947</identifier><identifier>PMID: 28866601</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adaptive control ; Adaptive neural control ; Adaptive systems ; Backstepping ; Basis functions ; Computer simulation ; Control systems ; Controllers ; Dynamical systems ; Feedback ; Feedback control ; Neural networks ; Nonlinear dynamical systems ; Nonlinear dynamics ; Nonlinear systems ; nonlower triangular nonlinear systems ; Observers ; Output feedback ; output-feedback control ; Radial basis function ; Stability analysis</subject><ispartof>IEEE transaction on neural networks and learning systems, 2018-08, Vol.29 (8), p.3658-3668</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-c3d01ef9a6e7a8aeb4cfba44ee3d5c6dd6370131c73d3be4f98091067d229b463</citedby><cites>FETCH-LOGICAL-c351t-c3d01ef9a6e7a8aeb4cfba44ee3d5c6dd6370131c73d3be4f98091067d229b463</cites><orcidid>0000-0001-5712-9356 ; 0000-0002-7149-5712 ; 0000-0002-8703-6967 ; 0000-0001-8316-5289</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8019887$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8019887$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28866601$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Huanqing</creatorcontrib><creatorcontrib>Liu, Peter Xiaoping</creatorcontrib><creatorcontrib>Li, Shuai</creatorcontrib><creatorcontrib>Wang, Ding</creatorcontrib><title>Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. 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Liu, Peter Xiaoping ; Li, Shuai ; Wang, Ding</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c351t-c3d01ef9a6e7a8aeb4cfba44ee3d5c6dd6370131c73d3be4f98091067d229b463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adaptive control</topic><topic>Adaptive neural control</topic><topic>Adaptive systems</topic><topic>Backstepping</topic><topic>Basis functions</topic><topic>Computer simulation</topic><topic>Control systems</topic><topic>Controllers</topic><topic>Dynamical systems</topic><topic>Feedback</topic><topic>Feedback control</topic><topic>Neural networks</topic><topic>Nonlinear dynamical systems</topic><topic>Nonlinear dynamics</topic><topic>Nonlinear systems</topic><topic>nonlower triangular nonlinear systems</topic><topic>Observers</topic><topic>Output feedback</topic><topic>output-feedback control</topic><topic>Radial basis function</topic><topic>Stability analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Huanqing</creatorcontrib><creatorcontrib>Liu, Peter Xiaoping</creatorcontrib><creatorcontrib>Li, Shuai</creatorcontrib><creatorcontrib>Wang, Ding</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Huanqing</au><au>Liu, Peter Xiaoping</au><au>Li, Shuai</au><au>Wang, Ding</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>29</volume><issue>8</issue><spage>3658</spage><epage>3668</epage><pages>3658-3668</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. 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| subjects | Adaptive control Adaptive neural control Adaptive systems Backstepping Basis functions Computer simulation Control systems Controllers Dynamical systems Feedback Feedback control Neural networks Nonlinear dynamical systems Nonlinear dynamics Nonlinear systems nonlower triangular nonlinear systems Observers Output feedback output-feedback control Radial basis function Stability analysis |
| title | Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics |
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