Larbi Assem Moulai
Papers
1
Total Citations
2
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1
About
Larbi Assem Moulai is a researcher at the forefront of advanced motor control systems, with a primary focus on the integration of deep reinforcement learning (DRL) into permanent magnet synchronous motor (PMSM) drives. His work addresses critical challenges in modern electric drives, particularly in optimizing control strategies for high-performance applications. Moulai’s most-cited paper, "Implementation of deep reinforcement learning in permanent magnet synchronous motors control: A review" (2025), provides a comprehensive synthesis of how DRL algorithms—such as deep Q-networks and policy gradient methods—are revolutionizing PMSM control by enabling adaptive, model-free solutions that outperform traditional PID and field-oriented control. This review, already garnering 2 citations shortly after publication, serves as a vital resource for engineers and researchers seeking to bridge artificial intelligence with power electronics. Moulai’s contributions are particularly impactful in the context of electric vehicles and industrial automation, where efficient, real-time motor control is paramount. His work not only highlights the potential of DRL to handle nonlinearities and uncertainties in motor dynamics but also outlines future directions for robust, energy-efficient systems. As a rising voice in this interdisciplinary field, Moulai is shaping the next generation of intelligent motor drives.
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Top Papers
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