Dual Three-Phase PMSM Torque Modeling and Maximum Torque per Peak Current Control Through Optimized Harmonic Current Injection
Bibliographic record
Abstract
Vector space decomposition (VSD) model is widely used for dual three-phase permanent magnet synchronous machine (dual-PMSM) control, in which two direct-quadrature (DQ) frames, DQ1 and DQ2, are introduced to facilitate the controller design. Existing studies show that harmonic current injection in DQ2 frame can increase the output torque for a given peak phase current, which is referred as maximum torque per peak current (MTPPC) control. However, the injected harmonic current will induce a small dc torque and the harmonic torque. This paper first proposes a comprehensive torque model considering the harmonics in PM flux linkages, inductances and stator currents to investigate the induced torque components, which are neglected in existing approaches. These torque components are then considered in the harmonic current design to improve the MTPPC control performance. The harmonic current design results in a multiobjective optimization problem, and genetic algorithm (GA) is employed to optimize the harmonic current to maximize the output torque with minimal torque harmonic. Compared with existing approaches, the proposed approach is applicable to both surface-mounted and interior dual-PMSMs. Experimental investigations on a laboratory interior dual-PMSM show that the output torque of the test motor can be increased by more than ten percent with a negligible increase in torque ripple.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".