Loss Minimization Algorithm for Surface-Mounted PMSM Using Ripple-Based Extremum Seeking
Bibliographic record
Abstract
In this article, a fast and parameter-intensive loss minimization algorithm (LMA) is proposed for the surface-mounted permanent magnet synchronous machine (PMSM). The algorithm utilizes ripple correlation control to steer the operating point toward the optimal solution by evaluating the correlation between the injected ripple on the control variable and its effect on the input power. Unlike model-based LMAs, this method does not rely on the motor loss model, its parameters, or precomputed information. Instead, it employs a high-speed search-based procedure to minimize the input power directly. The theoretical analysis includes a design procedure and a method for determining the upper bound of the injected ripple frequency based on the principles underlying loss minimization of PMSMs. Since thed-axis current does not contribute to torque production in surface-mounted PMSMs, the artificial perturbation introduced by the algorithm does not result in undesirable torque ripple. The analysis is supported by simulations and experimental tests. The results demonstrate that the proposed algorithm enables the system to converge to the optimum point within around 1.5 s, making it suitable for high-dynamic applications.
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 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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".