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Sensorless control of interior permanent-magnet machine drives with zero-phase lag position estimation
Abstract
This paper presents an improved method to estimate rotor motion states for an interior permanent-magnet machine drive. This approach is based on the estimation of a saliency-based electromotive force (EMF) in the stationary reference frame using a state filter. The spatial information obtained from the estimated saliency-based EMF is used in an observer to estimate the motor motion states. By using the commanded torque as a feedforward input to the observer, the motion state estimation has zero-phase lag, providing a very-high-bandwidth estimate.
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Cited by
Position Sensorless Permanent Magnet Synchronous Machine Drives—A Review
Wang, G., Valla, M., & Solsona, J. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2955409 · Cited by 855
Jeong, Y.-S., Sul, S.-K., Schulz, S. E., & Patel, N. R. (2005) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2004.840947 · Cited by 240
Bhuiyan, E. A., Akhand, M. M. A., Das, S. K., Ali, M. F., Tasneem, Z., Islam, M. R., Saha, D. K., Badal, F. R., Ahamed, M. H., & Moyeen, S. I. (2020) · International Journal of Automation and Computing · doi:10.1007/s11633-020-1250-3 · Cited by 68
Metadata
- Authors
- Kim, H., Harke, M. C., & Lorenz, R. D.
- Venue
- IEEE Transactions on Industry Applications
- Published
- 2003-11
- Volume
- 39
- Issue
- 6
- Pages
- 1726-1733
- ISSN
- 0093-9994 (ISSN-L)
- OpenAlex
- W2178900377
- Cited by
- 301
- References
- 0
- FWCI
- 12.40
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Sensorless control of interior permanent-magnet machine drives with zero-phase lag position estimation. https://app.cochat.ai/papers/10.1109/tia.2003.818966
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- Kim, H., Harke, M. C., & Lorenz, R. D. (2003). Sensorless control of interior permanent-magnet machine drives with zero-phase lag position estimation. IEEE Transactions on Industry Applications. https://doi.org/10.1109/tia.2003.818966
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
