Sensorless ANN-based control for permanent magnet synchronous machine drives
Bibliographic record
Abstract
In this paper, a sensorless artificial neural network (ANN) speed control strategy of permanent magnet synchronous machines (PMSMs) is introduced as an alternative to conventional control techniques. The control strategy achieves accurate tracking by making use of ANN's learning capabilities to approximate the machine's nonlinear dynamics. On the other hand, an ANN-based observer is used to estimate rotor speed and the rotor position is obtained by direct integration to reduce the effect of the system's noise. Unlike other sensorless control strategies, no a priori of œine training, weights initialization, voltage transducer or mechanical parameters knowledge is required. Furthermore, the stability of the overall closed-loop system is proved by Lyapunov stability theory. The controller is compared to the well-known vector control technique. Results for different situations highlight the higher performance of the proposed control approach in transient, steady-state, and standstill conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".