Improved Adaptive PI-like Fuzzy Control Strategy of Permanent Magnet Synchronous Motor

The fuzzy controller is a popular choice for permanent magnet synchronous motor (PMSM) control systems because of its advantages, such as straightforward design, and no reliance on the precise mathematical model of the motor. But the existing pure PI-like fuzzy control strategy still has some disadvantages, such as poor adaptive ability and large overshooting. This work redevelops the structure and rules of the adaptive fuzzy controller, and proposes and proves an improved adaptive PI-like fuzzy control algorithm for the PMSM system. Firstly, a parallel dual fuzzy controller structure is constructed to facilitate the adaptive adjustment of the “PI-like fuzzy controller”. Secondly, the error acceleration parameter rv(k), which contains the PMSM speed information, is set and normalized to accurately identify the dynamic response stages of the PMSM system. Lastly, an adaptive fuzzy rule table is designed based on the dynamic response waveform of the PMSM system, and the control characterization is analyzed. The simulation and experimental results of the PMSM system show that the improved adaptive PI-like fuzzy controller has a broad dynamic adjustment range, the PMSM can rapidly and smoothly reach the given speed during the startup stage with small overshooting, the speed drop is low when the load is abruptly added, the PMSM system can quickly return to the steady state with a strong adaptive ability, and its dynamic performance indicators surpass those of the PID controller and traditional PI-like fuzzy controller.

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Publication Details

Journal
Energies
Published
2025-01-16
DOI
https://doi.org/10.3390/en18020362
Citations
9
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
6.57

Funders

Controls
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article

Improved Adaptive PI-like Fuzzy Control Strategy of Permanent Magnet Synchronous Motor

Wenshao Bu, Zhun Fan, Shihao Guo, Jinwei Li
9 citations
Energies
Sensorless Control of Electric Motors
6.57
article

Improved Adaptive PI-like Fuzzy Control Strategy of Permanent Magnet Synchronous Motor

Wenshao Bu, Zhun Fan, Shihao Guo, Jinwei Li
article en
9 citations

Abstract

The fuzzy controller is a popular choice for permanent magnet synchronous motor (PMSM) control systems because of its advantages, such as straightforward design, and no reliance on the precise mathematical model of the motor. But the existing pure PI-like fuzzy control strategy still has some disadvantages, such as poor adaptive ability and large overshooting. This work redevelops the structure and rules of the adaptive fuzzy controller, and proposes and proves an improved adaptive PI-like fuzzy control algorithm for the PMSM system. Firstly, a parallel dual fuzzy controller structure is constructed to facilitate the adaptive adjustment of the “PI-like fuzzy controller”. Secondly, the error acceleration parameter rv(k), which contains the PMSM speed information, is set and normalized to accurately identify the dynamic response stages of the PMSM system. Lastly, an adaptive fuzzy rule table is designed based on the dynamic response waveform of the PMSM system, and the control characterization is analyzed. The simulation and experimental results of the PMSM system show that the improved adaptive PI-like fuzzy controller has a broad dynamic adjustment range, the PMSM can rapidly and smoothly reach the given speed during the startup stage with small overshooting, the speed drop is low when the load is abruptly added, the PMSM system can quickly return to the steady state with a strong adaptive ability, and its dynamic performance indicators surpass those of the PID controller and traditional PI-like fuzzy controller.

EnergiesVol. 18(2)
Henan University of Science and Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 4%
Sensorless Control of Electric Motors
6.57
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Improved Adaptive PI-like Fuzzy Control Strategy of Permanent Magnet Synchronous Motor — Wenshao Bu, Zhun Fan, et al. · Energies (2025) | TGRS Research Map | TGRS