Sensorless Control of PMSM Based on an Improved Super-Twisting Sliding Mode Observer
Bibliographic record
Abstract
This paper presents an enhanced fast super-twisting algorithm sliding mode observer for sensorless control of permanent magnet synchronous motors. The proposed method improves upon conventional super-twisting algorithms by incorporating a linear correction term, which simultaneously boosts convergence speed and reduces chattering effects. These enhancements lead to superior dynamic response and steady-state estimation accuracy. Stability is guaranteed through rigorous Lyapunov analysis, with explicit convergence conditions mathematically derived. For practical implementation, rotor position and speed are accurately extracted from the estimated extended back-EMF using an optimized signal processing chain combining low-pass filtering and quadrature phase-locked loop techniques. Comprehensive simulation studies validate the proposed observer's performance advantages over traditional super-twisting approaches, demonstrating significant improvements in both position and speed estimation accuracy. The results confirm the method's effectiveness and practical viability for high-performance sensorless PMSM 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".