The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Sensorless control of AC machines at low speed and standstill based on the "INFORM" method
Abstract
The increasing use of AC machines compared to DC motors in electrical drive applications has several reasons. A very important advantage of AC machines is their simple construction. However, AC drives often need mechanical sensors (tachometers, position encoders) for field orientation. In many applications these sensors reduce robustness and increase costs of a drive considerably. The main objective of this paper is to present the "INFORM" method based on real-time inductance measurements for sensorless control of AC drives.
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Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
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
Metadata
- Authors
- Schroedl, M.
- Venue
- IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting
- Publisher
- IEEE
- Published
- 2002-12-24
- Volume
- 1
- Pages
- 270-277
- ISSN
- 0197-2618 (ISSN-L)
- OpenAlex
- W2153739093
- Cited by
- 618
- References
- 1
- FWCI
- 44.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Sensorless control of AC machines at low speed and standstill based on the "INFORM" method. https://app.cochat.ai/papers/10.1109/ias.1996.557028
- The paper
- Schroedl, M. (2002). Sensorless control of AC machines at low speed and standstill based on the "INFORM" method. IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting. https://doi.org/10.1109/ias.1996.557028
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
