Fault Tolerant Maximum Torque Per Ampere (FT-MTPA) Control for Dual Three-Phase Interior PMSMs Under Open-Phase Fault
Bibliographic record
Abstract
For the dual three-phase interior permanent magnet synchronous machines (DT-IPMSMs), open-phase fault can cause current unbalance and degrade the drive performance. This article proposes a fault tolerant maximum torque per ampere (FT-MTPA) control for DT-IPMSMs, which can maximize the ratio of the average torque to the stator current and minimize the fault-induced torque ripple. In the proposed approach, optimal FT-MTPA solution is derived and theoretically proven, and current rms is considered as one design constraint to ensure the equivalent loss to the healthy condition. The proposed FT-MTPA control ensures the smooth switching between fault tolerant control and healthy control without inducing noticeable torque ripple. Compared with existing methods, the proposed approach is computation-efficient and fast in achieving the FT-MTPA control, which is critical to practical applications with fast changing loads. Moreover, this article derives the optimal solution from the faulty MTPA model with fault induced terms considered to ensure high torque. Experiments and comparisons with existing methods are conducted to evaluate the proposed approach on a laboratory DT-IPMSM.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".