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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  2/2021 - 2

Adaptive Interval Type-2 Fuzzy Controller Based Direct Torque Control of Permanent Magnet Synchronous Motor

HENINI, N. See more information about HENINI, N. on SCOPUS See more information about HENINI, N. on IEEExplore See more information about HENINI, N. on Web of Science, TLEMCANI, A. See more information about  TLEMCANI, A. on SCOPUS See more information about  TLEMCANI, A. on SCOPUS See more information about TLEMCANI, A. on Web of Science, BARKAT, S. See more information about BARKAT, S. on SCOPUS See more information about BARKAT, S. on SCOPUS See more information about BARKAT, S. on Web of Science
 
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Download PDF pdficon (1,414 KB) | Citation | Downloads: 336 | Views: 315

Author keywords
adaptive control, fuzzy systems, measurement uncertainty, permanent magnet motor, torque control

References keywords
fuzzy(34), control(27), type(23), systems(17), interval(12), logic(10), adaptive(10), torque(7), cybern(6), synchronous(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-05-31
Volume 21, Issue 2, Year 2021, On page(s): 15 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.02002
Web of Science Accession Number: 000657126200002
SCOPUS ID: 85107709037

Abstract
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This paper develops an adaptive type-2 fuzzy logic controller for direct torque control of a permanent magnet synchronous motor. The type-2 fuzzy logic systems are used for modeling the unknown functions. The adaptive law proposed takes in consideration the compensation of reconstruction errors by adding a sliding mode term. This term ensures the stability and the robustness of the control system regardless the internal and external disturbances. The stability of closed-loop system was verified using Lyapunovs stability theorem. Moreover, the proposed control scheme guarantees that all involved signals are bounded. The effectiveness and the feasibility of the proposed control method are demonstrated by extensive presentation and discussion of simulation results.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 3,868 TCR
SCOPUS® Citations for all references: 4,837 TCR

Web of Science® Average Citations per reference: 117 ACR
SCOPUS® Average Citations per reference: 147 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-09-19 01:56 in 234 seconds.




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