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Microcontroller Implementation of Lookup-Tablebased Hall Sensor Correction for Improving Dynamic Performance for Brushless DC Motors

2025· article· en· W4412130359 on OpenAlexaff
Matthew Hasman, Ziliang Feng, Juri Jatskevich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDC motorMicrocontrollerHall effect sensorComputer scienceLookup tableElectrical engineeringElectronic engineeringComputer hardwareEngineeringMagnetOperating system

Abstract

fetched live from OpenAlex

Hall-sensor-controlled brushless DC (BLDC) motors are used in many applications due to their low cost, ease of control, and good torque/power characteristics. In an ideal case, the Hall sensors are spaced precisely 120 electrical degrees apart. However, in low-cost machines, there may be significant errors in the positioning of Hall sensors, leading to uneven conduction intervals and an increase in torque ripple. Previous research proposed averaging filters for balancing the Hall sensor signals and performance restoration. However, averaging filters also introduce a delay and may cause degradation of transient performance. This paper proposes a filtering approach combined with a lookup table (LUT) to remove the undesirable filter delay and improve performance during accelerations/decelerations. The proposed methodology is implemented on a microcontroller and demonstrated experimentally on a typical industrial BLDC motor, achieving significant improvement in transient performance over the previous methods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.523
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.244
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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