Position Sensorless Control of Dual Three-Phase PMSM Based on Improved Sliding Mode Observer

This paper presents an improved sensorless control system for dual three-phase permanent magnet synchronous motors (DTP-PMSMS), aimed at overcoming the limitations of conventional sliding mode observer (SMO) algorithms. Conventional SMO algorithms frequently encounter chattering and reduced accuracy caused by motor parameter variations, such as changes in phase resistance and inductance. The proposed method enhances the conventional SMO by incorporating an S-shaped switching function, which can significantly reduce chattering. Moreover, an online parameter identification method based on a model reference adaptive system (MRAS) is integrated into the control system. The dynamic adaptation of motor parameters ensures stable and precise position estimation. Compared with conventional SMO algorithms, the improved method demonstrates superior accuracy and robustness, even under motor parameter variations.

Authors

Institutions

Publication Details

Published
2024-11-26
DOI
https://doi.org/10.23919/icems60997.2024.10920946
Citations
2
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.66
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Position Sensorless Control of Dual Three-Phase PMSM Based on Improved Sliding Mode Observer

Mingyao Lin, Keman Lin, Anchen Yang
2 citations
Advanced Sensor and Control Systems
0.66
article

Position Sensorless Control of Dual Three-Phase PMSM Based on Improved Sliding Mode Observer

Mingyao Lin, Keman Lin, Anchen Yang
article en
2 citations

Abstract

This paper presents an improved sensorless control system for dual three-phase permanent magnet synchronous motors (DTP-PMSMS), aimed at overcoming the limitations of conventional sliding mode observer (SMO) algorithms. Conventional SMO algorithms frequently encounter chattering and reduced accuracy caused by motor parameter variations, such as changes in phase resistance and inductance. The proposed method enhances the conventional SMO by incorporating an S-shaped switching function, which can significantly reduce chattering. Moreover, an online parameter identification method based on a model reference adaptive system (MRAS) is integrated into the control system. The dynamic adaptation of motor parameters ensures stable and precise position estimation. Compared with conventional SMO algorithms, the improved method demonstrates superior accuracy and robustness, even under motor parameter variations.

Hohai University (CN), Southeast University (CN)
Affordable and clean energy
Openalex Percentile: Top 27%
Advanced Sensor and Control Systems
0.66
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.