Adaptive neural control for output-constrained pure-feedback systems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, B.S. | - |
dc.contributor.author | Yoo, S.J. | - |
dc.date.available | 2019-03-09T00:41:02Z | - |
dc.date.issued | 2014-01 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/13886 | - |
dc.description.abstract | This paper investigates an adaptive approximation design problem for the tracking control of output-constrained non-affine pure-feedback systems. To satisfy the desired performance without constraint violation, we employ a barrier Lyapunov function which grows to infinity whenever its argument approaches some limits. The main difficulty in dealing with pure-feedback systems considering output constraints is that the system has a non-affine appearance of the constrained variable to be used as a virtual control. To overcome this difficulty, the implicit function theorem and mean value theorem are exploited to assert the existence of the desired virtual and actual controls. The function approximation technique based on adaptive neural networks is used to estimate the desired control inputs. It is shown that all signals in the closed-loop system are uniformly ultimately bounded. © ICROS 2014. | - |
dc.format.extent | 6 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 제어·로봇·시스템학회 | - |
dc.title | Adaptive neural control for output-constrained pure-feedback systems | - |
dc.title.alternative | Adaptive Neural Control for Output-Constrained Pure-Feedback Systems | - |
dc.type | Article | - |
dc.identifier.doi | 10.5302/J.ICROS.2014.13.1972 | - |
dc.identifier.bibliographicCitation | Journal of Institute of Control, Robotics and Systems, v.20, no.1, pp 42 - 47 | - |
dc.identifier.kciid | ART001841067 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-84893903510 | - |
dc.citation.endPage | 47 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 42 | - |
dc.citation.title | Journal of Institute of Control, Robotics and Systems | - |
dc.citation.volume | 20 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Adaptive neural control | - |
dc.subject.keywordAuthor | Barrier lyapunov function | - |
dc.subject.keywordAuthor | Non-affine | - |
dc.subject.keywordAuthor | Pure-feedback systems | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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