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<p>Permanent Magnet Synchronous Motors (PMSM) are being increasingly employed in high-performance industrial environments that require precise, rapid, and efficient speed regulation. This study introduces a groundbreaking technique for controlling PMSM speed by integrating a Takagi–Sugeno Fuzzy Controller (TSFC) with an African Buffalo Optimization (ABO)-enhanced adaptive Deep Belief Network (DBN). The adaptive DBN dynamically adjusts the number of hidden neurons and fine-tunes weight parameters using ABO, while the TSFC, enhanced by the optimized DBN, addresses steady-state error through improved fuzzy rules and membership functions. By utilizing a soft-sign activation function, this approach achieves a minimal mean square error in estimating rated current. Performance evaluations indicate notable enhancements: a 25.3% reduction in rise time, a 21.59% decrease in settling time, peak overshoot constrained to 93.2%, and an effectively zero steady-state error—outperforming heuristic and conventional controllers found in the literature. The method is crucial for high-performance PMSM applications because it is adaptable, works quickly, and handles changes well.</p>

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Utilizing Takagi Sugeno Fuzzy Controller Based African Buffalo Optimized Generative Deep Belief Network for Electric Vehicle Applications Employing Permanent Magnet Synchronous Motors

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Vijay Amirtha Raj F,
  • Iyappan Murugesan,
  • Mohankumar J,
  • Rajasundrapandiyanleebanon T

摘要

Permanent Magnet Synchronous Motors (PMSM) are being increasingly employed in high-performance industrial environments that require precise, rapid, and efficient speed regulation. This study introduces a groundbreaking technique for controlling PMSM speed by integrating a Takagi–Sugeno Fuzzy Controller (TSFC) with an African Buffalo Optimization (ABO)-enhanced adaptive Deep Belief Network (DBN). The adaptive DBN dynamically adjusts the number of hidden neurons and fine-tunes weight parameters using ABO, while the TSFC, enhanced by the optimized DBN, addresses steady-state error through improved fuzzy rules and membership functions. By utilizing a soft-sign activation function, this approach achieves a minimal mean square error in estimating rated current. Performance evaluations indicate notable enhancements: a 25.3% reduction in rise time, a 21.59% decrease in settling time, peak overshoot constrained to 93.2%, and an effectively zero steady-state error—outperforming heuristic and conventional controllers found in the literature. The method is crucial for high-performance PMSM applications because it is adaptable, works quickly, and handles changes well.