Robust Deadbeat Predictive Current Control with Online Inductance Identification for Low-inductance SPMSM
- Authors
- Dai, Jialiang; Kim, Hyunwoo; Ahn, Jungho; Lee, Ju; Yun, Inyeol
- Issue Date
- May-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Deadbeat predictive current control; online inductance identification; parameter robustness
- Citation
- 2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023, pp.1 - 4
- Indexed
- SCOPUS
- Journal Title
- 2023 IEEE International Electric Machines and Drives Conference, IEMDC 2023
- Start Page
- 1
- End Page
- 4
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192227
- DOI
- 10.1109/IEMDC55163.2023.10239004
- Abstract
- This paper expounds upon the implementation of a highly sophisticated deadbeat predictive current control (DPCC) that makes use of inductance identification to effectively regulate a low-inductance permanent magnet synchronous motor (PMSM). The principal objective of this groundbreaking control approach is to mitigate the existing current fluctuations in a manner that minimizes the computational burden, while simultaneously ensuring that additional inductance is not required. It is worth noting that the parameters inherent in the incremental prediction model display a curious pattern in which only the inductance mismatch makes a significant contribution to the prediction error, while the impact of resistance and permanent magnet (PM) flux linkage is comparatively negligible. As such, a meticulously designed inductance influence weighting factor has been devised to facilitate the assessment of the inductance error with a high degree of precision. Furthermore, the innovative DPCC with inductance identification methodology delivers substantially lower total harmonic distortion (THD) values when contrasted against the performance exhibited by the conventional FOC approach. © 2023 IEEE.
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