Adaptive Backstepping Based Online Loss Minimization Control of an IM Drive
Bibliographic record
Abstract
Among the numerous loss minimization algorithms (LMA), a loss-model-based approach offers a fast response without torque pulsations. However, it requires the accurate loss model and the knowledge of the motor parameters. Therefore, a technical difficulty in deriving the loss model-based controller (LMC) lies in the complexity of the full loss model and the on-line motor parameter adaptation. In an effort to overcome the drawbacks of LMC, this paper presents a new strategy for inverter-fed IM drives aiming for both high efficiency and high dynamic performance. A new LMC incorporating the effect of the leakage inductance and an adaptive backstepping based nonlinear controller (ABNC) are designed and combined with each other. Thus on-line parameter adaptation of LMC can be obtained with no extra effort. The proposed control scheme is implemented in real-time using digital signal processor board DS 1104 and simulation and experimental results demonstrate the effectiveness of the proposed scheme.
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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 | 0.000 |
| 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 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".