Improved Full-Order Adaptive Observer for Sensorless Induction Motor Control in Railway Traction Systems Under Low-Switching Frequency
Bibliographic record
Abstract
In the applications of medium- to high-power railway traction systems, the switching frequency and controller sampling rate are relatively low due to the requirements for low losses, high reliability, and limited processor power. These limitations greatly hinder the sensorless control of induction motors, especially in the high-speed operation range. In this paper, a full-order adaptive observer based on an improved discrete model is proposed, which significantly improves the system stability and control performance over the entire speed range. The feedback gain matrix of the presented control scheme is developed in a discrete time domain, ensuring that all the poles of the closed-loop observer are within the stable region. Moreover, a speed estimation mechanism based on Lyapunov's approach is designed in the synchronous rotating reference frame to further improve the precision of speed observation. Rigorous simulation studies and experimental tests of a benchmark induction motor in railway traction system demonstrate that the improved adaptive observer achieves superior sensorless control performance over the conventional adaptive observer.
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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 | 0.001 |
| 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".