Jing Rui Tang
Papers
1
Total Citations
2
H-Index
1
About
Jing Rui Tang is a researcher at the forefront of intelligent motor control, specializing in the integration of deep reinforcement learning with permanent magnet synchronous motor (PMSM) systems. Their most-cited work, "Implementation of deep reinforcement learning in permanent magnet synchronous motors control: A review" (2025), provides a comprehensive synthesis of how advanced AI algorithms can optimize torque, speed, and efficiency in electric drives—a critical area for applications ranging from electric vehicles to industrial automation. Though early in its citation trajectory, this review has already garnered attention for its forward-looking framework, bridging the gap between theoretical reinforcement learning models and practical motor control challenges. Tang’s contributions are particularly notable for systematically categorizing state-of-the-art methods, identifying key implementation barriers, and proposing future research directions, making it an essential reference for engineers and researchers navigating the convergence of machine learning and power electronics. As the demand for smarter, more adaptive motor systems grows, Tang’s work positions them as a rising voice in the field, with potential to shape next-generation control architectures.
Research Focus
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Top Papers
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