Deadbeat Harmonic Current Control of Permanent Magnet Synchronous Machine Drives for Torque Ripple Reduction
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Bibliographic record
Abstract
This article proposes a torque ripple reduction method based on speed ripples for permanent magnet synchronous machine (PMSM) drives. The torque ripples are first estimated based on speed harmonics at low speed. Based on that, the optimal harmonic current reference is then derived to fully compensate for the estimated torque ripple with minimum stator resistive loss. An extended state observer (ESO)-based deadbeat harmonic current controller (DHCC) is also presented in the rotor reference frame (RRF) to inject the optimal harmonic currents with good performance in steady state and transients. To extract the harmonic currents for harmonic current control and speed harmonics for torque ripple estimation with fast convergence and good accuracy, an adaptive linear neural (ADALINE) network-based filter is implemented in this article. Simulations and experiments are carried out to show the validity and performance of the proposed torque ripple reduction method.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it