Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network

Hasan Ali Gamal Al-Kaf; Sadeq Ali Qasem Mohammed; Kyo Beum Lee

doi:10.1109/SLED57582.2023.10261372

Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

Extended back electromotive force (EEMF) has been widely used as speed estimation of permanent magnet synchronous motors (PMSMs) due to its excellent performance at medium and high speeds. However, at low speed, EEMF method has low estimation accuracy. Several sensorless methods have combined EEMF with different accurate low-speed estimation techniques to attain a satisfactory estimation performance in the whole speed range. However, the transition between EEMF and low-speed estimation methods is a crucial step that requires two separate speed estimators with different structures. This leads to increased design complexity. Therefore, in this paper, a single sensorless full-speed control is proposed using an artificial neural network (ANN). The proposed ANN method does not require any transition method compared with hybrid full-speed sensorless methods. Simulation results show that proposed ANN can estimate rotor position for full speed compared with EEMF which estimates the rotor speed at medium and high speed.

Original language	English
Title of host publication	2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350335422
DOIs	https://doi.org/10.1109/SLED57582.2023.10261372
State	Published - 2023
Externally published	Yes
Event	2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023 - Seoul, Korea, Republic of Duration: 16 Aug 2023 → 18 Aug 2023

Publication series

Name	2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023

Conference

Conference	2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023
Country/Territory	Korea, Republic of
City	Seoul
Period	16/08/23 → 18/08/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Extended back electromotive force
PMSM
Sensorless methods
artificial neural network

ASJC Scopus subject areas

Electrical and Electronic Engineering
Mechanical Engineering
Computational Mechanics

Access to Document

10.1109/SLED57582.2023.10261372

Cite this

Al-Kaf, H. A. G., Mohammed, S. A. Q., & Lee, K. B. (2023). Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network. In 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023 (2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SLED57582.2023.10261372

Al-Kaf, Hasan Ali Gamal ; Mohammed, Sadeq Ali Qasem ; Lee, Kyo Beum. / Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network. 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023).

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title = "Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network",

abstract = "Extended back electromotive force (EEMF) has been widely used as speed estimation of permanent magnet synchronous motors (PMSMs) due to its excellent performance at medium and high speeds. However, at low speed, EEMF method has low estimation accuracy. Several sensorless methods have combined EEMF with different accurate low-speed estimation techniques to attain a satisfactory estimation performance in the whole speed range. However, the transition between EEMF and low-speed estimation methods is a crucial step that requires two separate speed estimators with different structures. This leads to increased design complexity. Therefore, in this paper, a single sensorless full-speed control is proposed using an artificial neural network (ANN). The proposed ANN method does not require any transition method compared with hybrid full-speed sensorless methods. Simulation results show that proposed ANN can estimate rotor position for full speed compared with EEMF which estimates the rotor speed at medium and high speed.",

keywords = "Extended back electromotive force, PMSM, Sensorless methods, artificial neural network",

author = "Al-Kaf, \{Hasan Ali Gamal\} and Mohammed, \{Sadeq Ali Qasem\} and Lee, \{Kyo Beum\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023 ; Conference date: 16-08-2023 Through 18-08-2023",

year = "2023",

doi = "10.1109/SLED57582.2023.10261372",

language = "English",

series = "2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023",

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Al-Kaf, HAG , Mohammed, SAQ & Lee, KB 2023, Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network. in 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023. 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023, Seoul, Korea, Republic of, 16/08/23. https://doi.org/10.1109/SLED57582.2023.10261372

Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network. / Al-Kaf, Hasan Ali Gamal ; Mohammed, Sadeq Ali Qasem; Lee, Kyo Beum.
2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network

AU - Al-Kaf, Hasan Ali Gamal

AU - Mohammed, Sadeq Ali Qasem

AU - Lee, Kyo Beum

PY - 2023

Y1 - 2023

N2 - Extended back electromotive force (EEMF) has been widely used as speed estimation of permanent magnet synchronous motors (PMSMs) due to its excellent performance at medium and high speeds. However, at low speed, EEMF method has low estimation accuracy. Several sensorless methods have combined EEMF with different accurate low-speed estimation techniques to attain a satisfactory estimation performance in the whole speed range. However, the transition between EEMF and low-speed estimation methods is a crucial step that requires two separate speed estimators with different structures. This leads to increased design complexity. Therefore, in this paper, a single sensorless full-speed control is proposed using an artificial neural network (ANN). The proposed ANN method does not require any transition method compared with hybrid full-speed sensorless methods. Simulation results show that proposed ANN can estimate rotor position for full speed compared with EEMF which estimates the rotor speed at medium and high speed.

AB - Extended back electromotive force (EEMF) has been widely used as speed estimation of permanent magnet synchronous motors (PMSMs) due to its excellent performance at medium and high speeds. However, at low speed, EEMF method has low estimation accuracy. Several sensorless methods have combined EEMF with different accurate low-speed estimation techniques to attain a satisfactory estimation performance in the whole speed range. However, the transition between EEMF and low-speed estimation methods is a crucial step that requires two separate speed estimators with different structures. This leads to increased design complexity. Therefore, in this paper, a single sensorless full-speed control is proposed using an artificial neural network (ANN). The proposed ANN method does not require any transition method compared with hybrid full-speed sensorless methods. Simulation results show that proposed ANN can estimate rotor position for full speed compared with EEMF which estimates the rotor speed at medium and high speed.

KW - Extended back electromotive force

KW - PMSM

KW - Sensorless methods

KW - artificial neural network

UR - https://www.scopus.com/pages/publications/85174573631

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DO - 10.1109/SLED57582.2023.10261372

M3 - Conference contribution

AN - SCOPUS:85174573631

T3 - 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023

BT - 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023

Y2 - 16 August 2023 through 18 August 2023

ER -

Al-Kaf HAG , Mohammed SAQ, Lee KB. Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network. In 2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 IEEE International Symposium on Sensorless Control for Electrical Drives, SLED 2023). doi: 10.1109/SLED57582.2023.10261372

Full-Speed Sensorless Control Scheme for Permanent Magnet Synchronous Motor Using Artificial Neural Network

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this