One new model based predictive torque control algorithm for doubly salient permanent magnet synchronous machines
Bibliographic record
Abstract
The doubly salient permanent-magnet synchronous machine (DSPMSM) is a new type of brushless machine with permanent magnet locating in its stator pole. Compared with other traditional PMSMs, it can offer advantages of high power/torque density, simple mechanical structure and wide speed range for high speed cruising, which is attractive to the applications of wind energy, plug-in hybrid electrical vehicle, etc. However, due to the nature of salient poles in both the stator and rotor, the DSPMSM suffers from severe torque and flux ripples for its variable magnetic circuits and equivalent air gap length. The conventional switching-table-based direct torque control (DTC) receives increasing attention for its merits of quick dynamic response, strong robustness and simple control structure. However, during the conventional DTC algorithm, large ripple of both torque and air gap flux often occurs for its hysteresis control based on Bang-Bang modification principle. This paper presents one improved strategy to reduce the torque ripple of DSPMSM drive system by the help of model based predictive torque control (MPTC), which is an improved algorithm in the base of conventional DTC. Similar as the traditional MPTC strategy, the new algorithm still requires one completely decoupling control scheme. By selecting the best voltage vector to satisfy the demands of torque and flux, the new method can obviously reduce both torque and flux ripples. Comprehensive simulation results are finally presented to validate theoretical analysis, and further experiments will be available in the near future.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".