Model-Free Predictive Current Control for PMSM Driven by ANPC Inverter with Super-Twisting Integral Sliding Mode Observer

To address the issue of control accuracy degradation caused by parameter mismatch in a permanent magnet synchronous motor (PMSM) driven by an active neutral point clamped (ANPC) three-level inverter, a model-free predictive current control strategy based on a super-twisting integral sliding mode observer (MFPCC-STISMO) is proposed. A parameter-independent first-order ultralocal model is constructed to replace the traditional PMSM mathematical model, and an STISMO with an integral sliding mode surface is designed to estimate the lumped disturbance, thereby suppressing the impact of parameter mismatches on motor control performance. Meanwhile, in the neutral point voltage balancing method, the traditional three-level model predictive current control (MPCC) relies on weighting factors, so a neutral point voltage balancing strategy without weighting factors is proposed. By leveraging the characteristic that a pair of positive and negative small vectors in the three-level inverter generate neutral point currents with equal magnitude and opposite directions, the complicated weighting factors adjustment process is eliminated through reasonable allocation of small vector action times. Finally, comparative experiments with traditional methods on an experimental platform show that the proposed method exhibits excellent dynamic and steady-state performance, and effectively enhances the disturbance rejection capability of the PMSM.

Authors

Institutions

Publication Details

Journal
IEEE Journal of Emerging and Selected Topics in Power Electronics
Published
2025-01-01
DOI
https://doi.org/10.1109/jestpe.2025.3609720
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 Predictive Current Control for PMSM Driven by ANPC Inverter with Super-Twisting Integral Sliding Mode Observer

Yipei Wang, Guangxu Zhou, Lianyu Li, Haoran Wang et al.
IEEE Journal of Emerging and Selected Topics in Power Electronics
Power Systems and Renewable Energy
article

Model-Free Predictive Current Control for PMSM Driven by ANPC Inverter with Super-Twisting Integral Sliding Mode Observer

Yipei Wang, Guangxu Zhou, Lianyu Li, Haoran Wang, Lei Guo, Zhenshi Sun, Guangwei Liu, Mengmei Zhu, Hongzhi Yang
article en

Abstract

To address the issue of control accuracy degradation caused by parameter mismatch in a permanent magnet synchronous motor (PMSM) driven by an active neutral point clamped (ANPC) three-level inverter, a model-free predictive current control strategy based on a super-twisting integral sliding mode observer (MFPCC-STISMO) is proposed. A parameter-independent first-order ultralocal model is constructed to replace the traditional PMSM mathematical model, and an STISMO with an integral sliding mode surface is designed to estimate the lumped disturbance, thereby suppressing the impact of parameter mismatches on motor control performance. Meanwhile, in the neutral point voltage balancing method, the traditional three-level model predictive current control (MPCC) relies on weighting factors, so a neutral point voltage balancing strategy without weighting factors is proposed. By leveraging the characteristic that a pair of positive and negative small vectors in the three-level inverter generate neutral point currents with equal magnitude and opposite directions, the complicated weighting factors adjustment process is eliminated through reasonable allocation of small vector action times. Finally, comparative experiments with traditional methods on an experimental platform show that the proposed method exhibits excellent dynamic and steady-state performance, and effectively enhances the disturbance rejection capability of the PMSM.

IEEE Journal of Emerging and Selected Topics in Power Electronics
Qilu University of Technology (CN), Shenyang University of Technology (CN), Shandong Institute of Automation (CN), Shandong Academy of Sciences (CN)
Openalex Percentile: Top 74%
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.