The DOI matches its Crossref record.
Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification
Abstract
The performance of vector controlled induction motor drives without speed sensor is generally poor at very low speed. The reasons are offset and drift components in the acquired feedback signals, voltage distortions caused by the nonlinear behavior of the switching converter, and the increased sensitivity against model parameter mismatch. New modeling and identification techniques are proposed to overcome these problems. A pure integrator is employed for stator flux estimation which permits high estimation bandwidth. Compensation of the drift components is done by offset identification. The nonlinear voltage distortions are corrected by a self-adjusting inverter model. A further improvement is a novel method for on-line adaptation of the stator resistance. Experiments demonstrate smooth steady-state operation and high dynamic performance at extreme low speed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Foo, G., & Rahman, M. F. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2030820 · Cited by 367
Foo, G., & Rahman, M. F. (2010) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2009.2030815 · Cited by 198
Foo, G. H. B., & Rahman, M. F. (2010) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2009.2036354 · Cited by 190
Real-Time Detection of Interturn Faults in PM Drives Using Back-EMF Estimation and Residual Analysis
Leboeuf, N., Boileau, T., Nahid-Mobarakeh, B., Clerc, G., & Meibody-Tabar, F. (2011) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2011.2168929 · Cited by 92
Metadata
- Authors
- Holtz, J., & Quan, J.
- Venue
- Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248)
- Publisher
- IEEE
- Published
- 2002-11-13
- Volume
- 4
- Pages
- 2614-2621
- ISSN
- 0197-2618 (ISSN-L)
- OpenAlex
- W4252932691
- Cited by
- 354
- References
- 0
- FWCI
- 19.23
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification. https://app.cochat.ai/papers/10.1109/ias.2001.955988
- The paper
- Holtz, J., & Quan, J. (2002). Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and parameter identification. Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248). https://doi.org/10.1109/ias.2001.955988
Data sources
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.