Adaptive Backstepping Based Online Loss Minimization Control of an IM Drive
Why this work is in the frame
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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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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 | 0.001 | 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) | 0.000 | 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 it