Fardila Mohd Zaihidee
Papers
1
Total Citations
2
H-Index
1
About
Fardila Mohd Zaihidee is a researcher at the forefront of intelligent motor control systems, with a primary focus on the integration of deep reinforcement learning into permanent magnet synchronous motor (PMSM) drives. Her work addresses critical challenges in optimizing motor performance, efficiency, and adaptability for applications ranging from electric vehicles to industrial automation. Her most cited paper, "Implementation of deep reinforcement learning in permanent magnet synchronous motors control: A review" (2025), provides a comprehensive synthesis of how reinforcement learning algorithms can replace traditional control methods, offering adaptive, data-driven solutions that enhance torque and speed regulation. This review has already garnered early attention, reflecting its timely relevance in the evolving field of smart motor control. Zaihidee’s contributions are pivotal in bridging the gap between advanced machine learning techniques and practical electromechanical systems, paving the way for more autonomous and efficient motor operations. Her work is particularly valuable for students and researchers exploring the intersection of artificial intelligence and power electronics, as it not only surveys current methodologies but also identifies promising future directions for real-time control optimization.
Research Focus
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Top Papers
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