Model-Free Adaptive Fuzzy Sliding-Mode Observer Control for PMSM

To further improve the estimation accuracy of the super-spiral observer, this paper innovatively proposes a fuzzy model-free adaptive control (FMFAC) super-spiral observer. The control scheme exhibits improved estimation accuracy after incorporating a fuzzy model-free adaptive component. The main contributions of this paper are as follows: (1) to achieve comprehensive fuzzy parameters without model adaptation, and (2) to creatively summarize the fuzzy rule table according to the observer’s characteristics. Experimental results demonstrate that the control performance of the proposed observer surpasses that of the traditional hyper-spiral observer.

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Publication Details

Journal
Energies
Published
2025-04-08
DOI
https://doi.org/10.3390/en18081877
Citations
3
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
Field-Weighted Citation Impact
3.64

Funders

Controls
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article

Model-Free Adaptive Fuzzy Sliding-Mode Observer Control for PMSM

Tao Tao, Dehong Zhu, Bang-Fen Zhu
3 citations
Energies
Adaptive Control of Nonlinear Systems
3.64
article

Model-Free Adaptive Fuzzy Sliding-Mode Observer Control for PMSM

Tao Tao, Dehong Zhu, Bang-Fen Zhu
article en
3 citations

Abstract

To further improve the estimation accuracy of the super-spiral observer, this paper innovatively proposes a fuzzy model-free adaptive control (FMFAC) super-spiral observer. The control scheme exhibits improved estimation accuracy after incorporating a fuzzy model-free adaptive component. The main contributions of this paper are as follows: (1) to achieve comprehensive fuzzy parameters without model adaptation, and (2) to creatively summarize the fuzzy rule table according to the observer’s characteristics. Experimental results demonstrate that the control performance of the proposed observer surpasses that of the traditional hyper-spiral observer.

EnergiesVol. 18(8)
Jiangsu University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 8%
Adaptive Control of Nonlinear Systems
3.64
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