Adaptive Neural Network Control Using Nonlinear Information Gain for Permanent Magnet Synchronous Motors
DC Field | Value | Language |
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dc.contributor.author | You, Sesun | - |
dc.contributor.author | Gil, Jeonghwan | - |
dc.contributor.author | Kim, Wonhee | - |
dc.date.accessioned | 2022-01-25T02:41:12Z | - |
dc.date.available | 2022-01-25T02:41:12Z | - |
dc.date.issued | 2023-03 | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.issn | 2168-2275 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/54311 | - |
dc.description.abstract | In this study, an adaptive neural network (NN) control using nonlinear information (NI) gain for permanent magnet synchronous motors (PMSMs) is proposed to improve control and estimation performance. The proposed method consists of a nonlinear controller, a three-layer NN approximator, and NI gain. The nonlinear controller is designed via a backstepping procedure for position tracking. The commutation scheme is designed to implement the PMSM control without the direct-quadrature (DQ) transform. The three-layer NN approximator is designed to estimate the unknown complex function generated by the recursive backstepping process. The NI gains are designed to enhance the control and estimation performance according to the increased tracking errors owing to the load torque and the desired position variations. All of signals in the closed-loop system guarantee the semiglobal uniformly ultimately boundness (UUB) using the Lyapunov stability theorem and the input-to-state stability (ISS) property. The performance of the proposed method was validated by experiments performed using a PMSM testbed. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Adaptive Neural Network Control Using Nonlinear Information Gain for Permanent Magnet Synchronous Motors | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TCYB.2021.3123614 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON CYBERNETICS, v.53, no.3, pp 1392 - 1404 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000732234600001 | - |
dc.identifier.scopusid | 2-s2.0-85149104271 | - |
dc.citation.endPage | 1404 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1392 | - |
dc.citation.title | IEEE TRANSACTIONS ON CYBERNETICS | - |
dc.citation.volume | 53 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Backstepping | - |
dc.subject.keywordAuthor | Artificial neural networks | - |
dc.subject.keywordAuthor | Torque | - |
dc.subject.keywordAuthor | Upper bound | - |
dc.subject.keywordAuthor | Estimation error | - |
dc.subject.keywordAuthor | Adaptive control | - |
dc.subject.keywordAuthor | Uncertainty | - |
dc.subject.keywordAuthor | Adaptive control | - |
dc.subject.keywordAuthor | backstepping control | - |
dc.subject.keywordAuthor | permanent magnet synchronous motor (PMSM) | - |
dc.subject.keywordAuthor | position control | - |
dc.subject.keywordPlus | DYNAMIC SURFACE CONTROL | - |
dc.subject.keywordPlus | SPEED CONTROL | - |
dc.subject.keywordPlus | FEEDBACK-CONTROL | - |
dc.subject.keywordPlus | CONTROL DESIGN | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Cybernetics | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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