Adaptive Step-Size Predictive PLL Based Rotor Position Estimation Method for Sensorless IPMSM Drives
Bibliographic record
Abstract
The fixed-gain position observer for sensorless interior permanent magnet synchronous motor (IPMSM) drives requires repeated trials for parameter tuning and has poor dynamic response capability. A novel adaptive step-size predictive phase-locked loop (ASS-PPLL) based rotor position estimation method is proposed to improve dynamic performance in this paper. A cost function using position tracking error decoupled from a high-frequency current response through a high-frequency square-wave injection is established. In addition, the step-size and direction are automatically adjusted by the pre-defined cost function to speed up the iterative search for an optimal rotor position estimate in a finite position set. Compared with the fixedgain observers, the proposed ASS-PPLL effectively improves dynamic performance without a complex and time-consuming parameter tuning process. Compared with the conventional predictive PLL, the proposed method reduces the computational burden with fewer iterations, while ensuring the position estimation accuracy. Finally, the effectiveness of the proposed ASS-PPLL is comprehensively verified on a 2.2-kW IPMSM drive platform.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".