Model-Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on Ultralocal Model

Parameter mismatches can adversely affect the control performance of model predictive current control. To address this issue, this paper proposes a model-free current predictive control method for permanent magnet synchronous motors (PMSM) based on a ultralocal model. This method employs a modified Extended State Observer (ESO) to estimate the motor system parameters using the ultralocal model. The model development process considers the impact of inductance parameters on the observer gain. By establishing a dynamic observer coefficient equation in the stationary two-phase reference frame, an observer gain online update mechanism is obtained that does not require the inductance parameter values, thereby enhancing the robustness of the model predictive current control strategy against inductance parameter variations. Simulation results demonstrate that the proposed method exhibits excellent parameter robustness, rapid dynamic response, and satisfactory steady-state performance

Authors

Institutions

Publication Details

Published
2024-11-26
DOI
https://doi.org/10.23919/icems60997.2024.10921097
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Model-Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on Ultralocal Model

Mohan Yang, Kexin Yao, Haobo Xu, Hongfeng Li
Power Systems and Renewable Energy
article

Model-Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on Ultralocal Model

Mohan Yang, Kexin Yao, Haobo Xu, Hongfeng Li
article en

Abstract

Parameter mismatches can adversely affect the control performance of model predictive current control. To address this issue, this paper proposes a model-free current predictive control method for permanent magnet synchronous motors (PMSM) based on a ultralocal model. This method employs a modified Extended State Observer (ESO) to estimate the motor system parameters using the ultralocal model. The model development process considers the impact of inductance parameters on the observer gain. By establishing a dynamic observer coefficient equation in the stationary two-phase reference frame, an observer gain online update mechanism is obtained that does not require the inductance parameter values, thereby enhancing the robustness of the model predictive current control strategy against inductance parameter variations. Simulation results demonstrate that the proposed method exhibits excellent parameter robustness, rapid dynamic response, and satisfactory steady-state performance

Tianjin University (CN), Huazhong University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 67%
Power Systems and Renewable Energy
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.