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@article{184475,
        author = {Mr. Ashutosh Balasaheb Ghodke and Prof. M.K.Neharkar and Prof. Dr. S.V. Yerigeri},
        title = {Performance Assessment of BLDC Motor Drives Under ANN and PID Control Strategies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {1632-1636},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184475},
        abstract = {Brushless DC (BLDC) motors are widely used in electric vehicles, industrial automation, and renewable energy systems due to their high efficiency, compact size, and superior dynamic response. However, their performance is highly dependent on effective control strategies. Traditional Proportional–Integral–Derivative (PID) controllers are widely applied due to their simple structure and ease of implementation, but they often struggle with nonlinearities, parameter variations, and external disturbances. On the other hand, Artificial Neural Network (ANN)-based controllers offer adaptive learning capabilities that enhance robustness and dynamic performance under varying operating conditions. This paper presents a comparative performance assessment of BLDC motor drives controlled using PID and ANN strategies. Simulation studies are conducted to evaluate speed regulation, torque ripple, transient response, and steady-state accuracy. The results demonstrate that while PID controllers perform satisfactorily under nominal conditions, ANN controllers significantly improve adaptability, reduce overshoot, and enhance efficiency, making them suitable for modern intelligent drive applications.},
        keywords = {Sensorless PMBLDC Motor, Solar Photovoltaic (PV), Battery Storage, Zeta Converter etc.},
        month = {September},
        }