Virtual Hybrid Voltage Injection-Based Sensorless Control of Surface PMSM Drives for Zero-Speed Operation
The anisotropy of surface-mounted permanent magnet synchronous motors (SPMSMs) is not prominent, making it challenging to achieve position sensorless control operation at zero speed. To address this issue, this paper proposes a zero-speed operation strategy based on virtual hybrid voltage injection for SPMSM drives. By analyzing the local weak observability of state variables under different operating conditions of SPMSM, a zero-speed local weak observability criterion is established. According to this criterion, a full-order adaptive flux observer with virtual hybrid voltage injection is designed. The injection of virtual hybrid voltage signals consisting of the virtual bias voltage and the virtual sinusoidal voltage into the observer ensures the local weak observability of SPMSM state variables at zero speed. A parameter selection method for the virtual hybrid voltage signal is proposed, which determines the stable intervals for the frequency and coefficients of the virtual hybrid voltage signal. The proposed strategy requires no injection of any real signals and enables stable operation under rated load at zero speed. The effectiveness of the proposed method is validated on a 7.5-kW sensorless SPMSM drive platform.
Authors
- Jiajia Han (ORCID: https://orcid.org/0000-0002-2642-0252)
- Gaolin Wang (ORCID: https://orcid.org/0000-0002-0109-8420)
- Li Ding (ORCID: https://orcid.org/0000-0002-1827-1090)
- Siqi Wang (ORCID: https://orcid.org/0000-0001-6103-3330)
- K.F. Wang (ORCID: https://orcid.org/0009-0003-7938-3330)
- Dianguo Xu (ORCID: https://orcid.org/0000-0002-1594-8625)
- Guoqiang Zhang (ORCID: https://orcid.org/0000-0002-2976-5854)
- Qiwei Wang (ORCID: https://orcid.org/0000-0001-7345-5434)
Institutions
- Harbin Institute of Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Transportation Electrification
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1109/tte.2025.3604611
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00