Enhanced Adaptive Higher Order Sliding Mode Observer based Sensorless Control
Bibliographic record
Abstract
The traditional higher order sliding mode observer (HSMO) based sensorless control strategy uses a constant gain. However, very large gain value can decrease the output accuracy, while very small gain value will degrade the sliding mode observer’s stability. Therefore, under uncertain disturbances, an adaptive gain should be set for the higher order sliding mode observer. This paper proposes a variable universe fuzzy adaptive high order sliding mode observer based sensorless control (VUF-HSMO) strategy for permanent magnet synchronous machines (PMSMs) to achieve high precision sensorless control. Firstly, fuzzy logic control technique is used to get an adaptive gain in the HSMO. Then, variable universe theory is employed to self-tune the universe of discourse according to the change of inputs, and this results in the improvement in position estimation accuracy. Experiment results under different conditions demonstrate the effectiveness and the superiority of the proposed VUF-HSMO based sensorless control compared with the traditional HSMO and fuzzy logic based HSMO.
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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".