Enhanced Speed Control of PMSM Using an Optimized SRUKF Algorithm

An improved square root unscented Kalman filter (SRUKF) algorithm is proposed to reduce the motor speed error in the presence of interference. In the simulation experiment, interference is added to affect the motor operation. The d − qmotor speed value is predicted using information such as the motor's shaft current, electrical angular velocity and rotor position. The improved algorithm is used to control the speed to reduce the error caused by the interference on the motor speed, and the algorithm is compared with the traditional unscented Kalman filter algorithm. The results show that the algorithm has less system computation, and the error of the motor speed with interference controlled by the algorithm is smaller than the true value without interference, and it has better robustness.

Authors

Publication Details

Journal
Scientific insights and discoveries review
Published
2024-10-11
DOI
https://doi.org/10.59782/sidr.v3i1.70
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhanced Speed Control of PMSM Using an Optimized SRUKF Algorithm

Shuo Xu, Hongzhi Wang, Hu Huangshui, Lan Shuishui et al.
Scientific insights and discoveries review
Sensorless Control of Electric Motors
article

Enhanced Speed Control of PMSM Using an Optimized SRUKF Algorithm

Shuo Xu, Hongzhi Wang, Hu Huangshui, Lan Shuishui, Lyu Hongwu
article en

Abstract

An improved square root unscented Kalman filter (SRUKF) algorithm is proposed to reduce the motor speed error in the presence of interference. In the simulation experiment, interference is added to affect the motor operation. The d − qmotor speed value is predicted using information such as the motor's shaft current, electrical angular velocity and rotor position. The improved algorithm is used to control the speed to reduce the error caused by the interference on the motor speed, and the algorithm is compared with the traditional unscented Kalman filter algorithm. The results show that the algorithm has less system computation, and the error of the motor speed with interference controlled by the algorithm is smaller than the true value without interference, and it has better robustness.

Scientific insights and discoveries reviewVol. 3
Affordable and clean energy
Openalex Percentile: Top 85%
Sensorless Control of Electric Motors
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.