Implementation of a Neural PI Controller for PMSM Drive Systems: FPGA Based Modeling and Experimental Validation
VERSION OF RECORD ONLINE: 08/09/2025
Corressponding author's email:
vuquynh@lhu.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1825Keywords:
PMSM, PI controller, Neural network controller, Sensorless, ExperimentalAbstract
This paper proposes a self-tuning PI control method using a neural network-based approach (Neural PI Controller - NPIC) for the Permanent Magnet Synchronous Motor (PMSM) drive system. First, the mathematical model of the PMSM is established and thoroughly analyzed to provide a solid theoretical foundation for control design. To enhance system performance and adaptability to dynamic uncertainties, a self-tuning PI controller (PIC) based on a Radial Basis Function Neural Network (RBF-NN) is developed to automatically adjust control parameters in real-time. Subsequently, the algorithm is implemented using Very High Speed Integrated Circuit Hardware Description Language (VHDL) and deployed on an FPGA to validate its functionality. Finally, experimental results demonstrate that the proposed controller enables the sensorless PMSM system to achieve fast and stable speed response with minimal oscillations, even under sudden load variations. These findings confirm the accuracy and effectiveness of the proposed method in real-world applications, providing a promising solution for high-performance PMSM control in industrial and automation systems.
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