2022
DOI: 10.1109/tec.2021.3125494
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Position Sensorless Control of Switched Reluctance Motor Drives Based on a New Sliding Mode Observer Using Fourier Flux Linkage Model

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Cited by 23 publications
(2 citation statements)
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“…It can be seen from ( 1)-( 3), it is the key to establish the SRM model to obtain the flux-linkage characteristics accurately. An analytical flux-linkage model presented in Refs [24][25][26] was adopted in this study. The analytical model approximates the flux-linkage in the form of Fourier series and polynomial multiplication, which can be expressed as…”
Section: Srm Modelmentioning
confidence: 99%
See 1 more Smart Citation
“…It can be seen from ( 1)-( 3), it is the key to establish the SRM model to obtain the flux-linkage characteristics accurately. An analytical flux-linkage model presented in Refs [24][25][26] was adopted in this study. The analytical model approximates the flux-linkage in the form of Fourier series and polynomial multiplication, which can be expressed as…”
Section: Srm Modelmentioning
confidence: 99%
“…It can be seen from (1)–(3), it is the key to establish the SRM model to obtain the flux‐linkage characteristics accurately. An analytical flux‐linkage model presented in Refs [24–26] was adopted in this study. The analytical model approximates the flux‐linkage in the form of Fourier series and polynomial multiplication, which can be expressed as ψp()ip,θgoodbreak=n=0Nλn()ipitaliccos()nNrθ1.6emitalicwith0.5emλn()ipgoodbreak=m=0Mbnmipm$$ {\psi}_p\left({i}_p,\theta \right)=\sum \limits_{n=0}^N{\lambda}_n\left({i}_p\right)\mathit{\cos}\left({nN}_r\theta \right)\kern1.6em with\kern0.5em {\lambda}_n\left({i}_p\right)=\sum \limits_{m=0}^M{b}_{nm}{i}_p^m $$ where N and M are the order of Fourier series and polynomial, respectively.…”
Section: Srm Modelmentioning
confidence: 99%