Year: 2025
Type: article
Abstract: The use of the reinforcement learning algorithm DQN(Deep Q-Network) can increase the design variables and offers the advantage of enabling more versatile motor optimization design. This paper evaluates the potential and advantages of reinforcement learning-based motor optimization design, which is still in its early stages globally. To demonstrate the performance of the proposed method, the geometry and drive conditions for reducing torque ripple of a FWSM(Field-Wound Synchronous Motor) using DQN were optimized. The proposed method exhibited excellent optimization performance, and it is expected that a more versatile optimization model can be developed by increasing the number of analysis cases.
Institution: Hoseo University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413973677