Levenberg–Marquardt Optimization-Based Fast-Convergent and Improved MTPA Control for PMSMs With Nonlinearity and Loss Compensation
Bibliographic record
Abstract
For permanent magnet synchronous machine (PMSM), maximum torque per ampere (MTPA) control is popular below rated speed, and deriving accurate control objective defined as the ratio of the torque to the stator current is critical to achieve high performance MTPA control. However, the derived control objective can be greatly affected by the system nonlinearity and loss such as core loss. This paper proposes a Levenberg-Marquardt (LM) optimization based fast and accurate MTPA control method with consideration of system nonlinearity and loss. At first, an improved MTPA control objective is derived, in which both the electrical and mechanical models are explored to develop the polynomial-based offset model for compensating the system nonlinearity and loss and improving the model accuracy. To avoid the slow convergence of conventional gradient descent method, this paper develops the LM based method with analytical gradient for fast MTPA control, in which the learning rate is adaptively adjusted to improve the convergence speed. The proposed approach is validated with experiments and comparisons on a test interior PMSMs under various operating conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".