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An Improved Sensorless PMSM Drive Performance Using Data-Driven ANFIS-SMO Framework
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AI
DOI：10.1007/s42835-026-02734-8.png)
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Abstract
En 中文
Accurate estimation of rotor position is vital for sensorless control of Permanent Magnet Synchronous Motor (PMSM) drive. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced Sliding Mode Observer (SMO) for sensorless control of PMSM is presented. SMO acts as a primary estimator for estimating back electromotive force (back-EMF), speed, and position, whereas ANFIS acts as a secondary estimator, serving as a post-processor to refine the estimated position for smooth sensorless control. Additionally, this proposed ANFIS-SMO-based observer performs well in the low-speed region, which is particularly critical for other observers. The proposed methodology is validated through simulation and experimental analysis for different dynamic conditions. From the analysis, it is observed that there is reduction in torque ripple, improvement in speed tracking, and a reduction of approximately 2% error in position estimation. These improvements made the proposed controller robust for sensorless control of PMSMs in various applications.
Keywords:
Permanent magnet synchronous motor
Intelligent control
Adaptive neuro fuzzy inference system
Sliding mode observer
Position estimation
Torque ripple
Journal
J
IF:
1.6
Papers:
286
Citations:
0
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