Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems
Issues of differentiation-free multiswitching neuroadaptive tracking control of strict-feedback systems are presented. It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2018-04, Vol.29 (4), p.1095-1107 |
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| creator | Huang, Jeng-Tze Pham, Thanh-Phong |
| description | Issues of differentiation-free multiswitching neuroadaptive tracking control of strict-feedback systems are presented. It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly bounded stability when the unknown nonlinearities are locally/globally linearly bounded, respectively. In particular, the so-called explosion of complexity is annihilated in two steps. First, a set of first-order low-pass filters are constructed for solving such a problem in the nominal neural compensators. In contrast to most existing dynamic surface control-based schemes, bounded stability of the filter dynamics is ensured by virtue of the localness and hence boundedness of the neural compensators. Separation of controller-filter pairs is thus achieved in this paper. Next, an auxiliary switched linear state feedback control is synthesized to further solve such a problem in the nonneural regions. Besides being differentiation-free, such an approach provides more flexibility for meeting various control objectives at a time. An earlier proposed smooth switching algorithm is also incorporated to tackle the control singularity problem. Finally, simulation works are presented to demonstrate the validity of the proposed scheme. |
| doi_str_mv | 10.1109/TNNLS.2017.2651903 |
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It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly bounded stability when the unknown nonlinearities are locally/globally linearly bounded, respectively. In particular, the so-called explosion of complexity is annihilated in two steps. First, a set of first-order low-pass filters are constructed for solving such a problem in the nominal neural compensators. In contrast to most existing dynamic surface control-based schemes, bounded stability of the filter dynamics is ensured by virtue of the localness and hence boundedness of the neural compensators. Separation of controller-filter pairs is thus achieved in this paper. Next, an auxiliary switched linear state feedback control is synthesized to further solve such a problem in the nonneural regions. Besides being differentiation-free, such an approach provides more flexibility for meeting various control objectives at a time. An earlier proposed smooth switching algorithm is also incorporated to tackle the control singularity problem. Finally, simulation works are presented to demonstrate the validity of the proposed scheme.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2017.2651903</identifier><identifier>PMID: 28186911</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adaptive systems ; Artificial neural networks ; Backstepping ; Compensators ; Computer simulation ; Control stability ; Control systems ; Differentiation ; Dynamic stability ; Dynamic surface control (DSC) ; Feedback ; Feedback control ; Low pass filters ; Neural networks ; neural networks (NNs) ; peaking phenomenon ; semiglobal stability ; smooth switching ; Stability criteria ; State feedback ; Surface stability ; switched linear control ; Switches ; Switching theory ; Tracking control ; Uncertainty</subject><ispartof>IEEE transaction on neural networks and learning systems, 2018-04, Vol.29 (4), p.1095-1107</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-832c7f660a4d50827effa82e3951fc911b290018f6f8800575ad8f48c277815c3</citedby><cites>FETCH-LOGICAL-c351t-832c7f660a4d50827effa82e3951fc911b290018f6f8800575ad8f48c277815c3</cites><orcidid>0000-0002-5246-8583</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7845704$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7845704$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28186911$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Huang, Jeng-Tze</creatorcontrib><creatorcontrib>Pham, Thanh-Phong</creatorcontrib><title>Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>Issues of differentiation-free multiswitching neuroadaptive tracking control of strict-feedback systems are presented. It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly bounded stability when the unknown nonlinearities are locally/globally linearly bounded, respectively. In particular, the so-called explosion of complexity is annihilated in two steps. First, a set of first-order low-pass filters are constructed for solving such a problem in the nominal neural compensators. In contrast to most existing dynamic surface control-based schemes, bounded stability of the filter dynamics is ensured by virtue of the localness and hence boundedness of the neural compensators. Separation of controller-filter pairs is thus achieved in this paper. Next, an auxiliary switched linear state feedback control is synthesized to further solve such a problem in the nonneural regions. 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It mainly consists of a set of nominal adaptive neural network compensators plus an auxiliary switched linear controller that ensures the semiglobally/globally ultimately uniformly bounded stability when the unknown nonlinearities are locally/globally linearly bounded, respectively. In particular, the so-called explosion of complexity is annihilated in two steps. First, a set of first-order low-pass filters are constructed for solving such a problem in the nominal neural compensators. In contrast to most existing dynamic surface control-based schemes, bounded stability of the filter dynamics is ensured by virtue of the localness and hence boundedness of the neural compensators. Separation of controller-filter pairs is thus achieved in this paper. Next, an auxiliary switched linear state feedback control is synthesized to further solve such a problem in the nonneural regions. 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| subjects | Adaptive systems Artificial neural networks Backstepping Compensators Computer simulation Control stability Control systems Differentiation Dynamic stability Dynamic surface control (DSC) Feedback Feedback control Low pass filters Neural networks neural networks (NNs) peaking phenomenon semiglobal stability smooth switching Stability criteria State feedback Surface stability switched linear control Switches Switching theory Tracking control Uncertainty |
| title | Differentiation-Free Multiswitching Neuroadaptive Control of Strict-Feedback Systems |
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