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Danial Kazemikia

Publications and source records attributed to Danial Kazemikia.

2 recordsLinked to original sources

A Parameterized Nonlinear Magnetic Equivalent Circuit for Design and Fast Analysis of Radial Flux Magnetic Gears with Bridges

Magnetic gears offer significant advantages over mechanical gears, including contactless power transfer, but require efficient and accurate modeling tools for optimization and commercialization. This paper presents the first fast and accurate 2D nonlinear magnetic equivalent circuit (MEC) model for radial flux magnetic gears (RFMG), capable of analyzing designs with bridges critical structural elements that introduce intense localized magnetic saturation. The proposed model systematically incorporates nonlinear effects while maintaining rapid simulation times through a parameterized geometry and adaptable flux tube distribution. A robust initialization strategy ensures reliable performance across diverse designs. Extensive validation against nonlinear finite element analysis (FEA) confirms the model's accuracy in torque and flux density predictions. A comprehensive parametric study of 140,000 designs demonstrates close agreement with FEA results, with simulations running up to 100 times faster. Unlike previous MEC approaches, this model provides a generalized, computationally efficient solution for analyzing a wide range of RFMG designs with or without bridges, making it particularly well-suited for large scale design optimization.

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Reinforcement Learning for Motor Control: A Comprehensive Review

Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state of RL in motor control, exploring various RL algorithms and applications. The review highlights RL's advantages, including model-free control, adaptability to changing conditions, and the ability to optimize for complex objectives. It also addresses challenges in applying RL to motor control, such as sim-to-real transfer, safety and stability concerns, scalability, and computational complexity. By providing a comprehensive overview of the field, this review aims to deepen understanding of RL's potential to revolutionize motor control and drive advancements across industries.

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