This paper proposes an artificial intelligence (AI)-assisted maximum-torque-per-ampere control of an interior permanent magnet synchronous machine (IPMSM). For torque control of an IPMSM, the optimum current trajectories are calculated. The current components are determined such that the magnitude of the current vector is minimized for a reference torque. Especially in the field weakening range, this results in nonlinear equations that have to be solved, which is why the current components are often calculated offline and stored discretely in look-up tables (LUTs). Due to the discrete storage of data, interpolation is performed for data not included in the LUT, resulting in interpolation errors. In this work, these LUTs are replaced by neural networks, minimizing this interpolation error. In addition to a significantly improved interpolation accuracy, the required memory space on a microcontroller is also minimized.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    AI-Assisted Torque Control of an Interior Permanent Magnet Synchronous Machine


    Contributors:


    Publication date :

    2023-11-28


    Size :

    1489189 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Interior permanent magnet motor torque extension control

    Shen, Xiangming / Zhou, Ronghui / Li, Yong et al. | IEEE | 2014


    Novel Effective Torque Analysis Method for Interior Permanent Magnet Synchronous Machines

    Zhuang, Haijun / Zuo, Shuguang / Ma, Zhixun et al. | IEEE | 2025


    A new approach to direct torque control of interior permanent magnet synchronous motor

    Chunmei Zhang, / Baozhu Ma, / Heping Liu, et al. | IEEE | 2006