Maximum Torque Per Ampere Control for IPMSM Using Gradient Descent Algorithm Based on Measured Speed Harmonics
Bibliographic record
Abstract
This paper proposes a novel gradient descent based maximum torque per ampere (MTPA) control algorithm for interior permanent magnet synchronous machines (IPMSMs) by using the measured speed harmonics. The proposed approach does not require machine parameters and thus is not influenced by the machine and drive nonlinearities. Hence, the proposed approach can ensure a robust MTPA control under different loading conditions. Specifically, in the proposed approach, a small q-axis harmonic voltage is injected into the machine to induce a small harmonic component in the machine speed. Based on the PMSM torque equation, the mathematical relation between the induced speed harmonic and the output torque is derived, which shows that the magnitude of the induced speed harmonic is proportional to the output torque of an IPMSM. Therefore, the speed harmonic is explored to seek the MTPA angle, in which the MTPA angle is found when the speed harmonic magnitude is maximized. In particular, the gradient descent algorithm is employed to detect the MTPA angle, which is computationally efficient and converges quickly. The proposed approach is evaluated with both simulations and experiments based on a laboratory IPMSM drive system.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".