Artificial Neural Network-Based PMSM Modeling for the Electric Motor Emulation
Bibliographic record
Abstract
Lookup table (LUT)-based modeling of electric machines using finite element analysis (FEA) is an accurate technique for high-fidelity real-time emulation of electric motors, however, the cost of a huge computational burden. To overcome this shortcoming and the need for expensive hardware for the motor emulation, an artificial neural network (ANN)-based modeling method is proposed and developed in this paper for a 22-kW permanent magnet synchronous machine (PMSM). The ANN-based machine model is trained using back-propagation algorithms and its weights are optimized for the given arbitrary input(s) to consider the non-linearities in a PMSM, which are supposed to be reflected in the LUTs implemented in the emulation system. A strong correlation with minimal error, after comparing the dq-axis currents and electromagnetic torques extracted from both the LUT-based model and proposed ANN-based model under various loading conditions, confirms the accuracy of the proposed computationally efficient ANN-based modeling method.
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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 | 0.000 |
| 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) | 0.002 | 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".