Optimal Current Modeling and Identification for Fast and Efficient Torque Ripple Minimization of PMSM Using Theoretical and Experimental Models
Bibliographic record
Abstract
For a permanent magnet synchronous machine (PMSM), torque ripple is a critical issue, especially under low speeds. Torque ripple minimization (TRM) control aims to find the optimal currents to be injected to cancel the existing torque ripple. In this article, we propose an optimal current modeling technique for the TRM control. In the proposed technique, torque harmonics are modeled by using a set of polynomials in which their degree parameters are determined by the theoretical torque model and their coefficients are determined by the experimental torque model. The optimal current model is derived from the identified polynomials to minimize the torque harmonics and machine loss in which there is no need of machine parameters. The proposed approach is both computation and memory efficient as the optimal currents are calculated from equations, which eliminates the need for the time-consuming optimization procedure and memory-consuming lookup table. The proposed approach is fast and efficient for the TRM control, which is critical to practical applications with fast-changing loads. Extensive experiments and comparisons are conducted on a laboratory PMSM to validate the proposed modeling technique under various operating conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".