Enhanced Sector Selection Method in FCS-MPC for Dual Three-Phase High-Saliency PMSMs
Bibliographic record
Abstract
The increasing demand for electrified transportation requires high-power, efficient, and reliable traction drive systems. Dual three-phase permanent magnet machines (DTP-PMSMs) with high saliency ratios offer key advantages for these applications but introduce challenges in control accuracy and current harmonic suppression. This paper proposes a novel finite control set model predictive control (FCS-MPC) strategy with an enhanced sector selection method specifically tailored for high-saliency DTP-PMSM drives. The proposed approach improves actuation accuracy in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> subspace by reliably identifying the optimal virtual vectors, while a quasi-proportional resonant (QPR) controller effectively suppresses current harmonics in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x-y</i> subspace. Simulation and experimental results demonstrate that the proposed method achieves a fast dynamic response, reduced current ripple, and strong current harmonic suppression, outperforming existing virtual vector-based FCS-MPC and field-oriented proportional-integral (PI) control methods. The results confirm the robustness and efficacy of the proposed controller across a wide range of operating conditions, making it a promising solution for high-performance electric drives.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".