Loss minimization control of interior permanent magnet synchronous motor drive using adaptive backstepping technique
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Bibliographic record
Abstract
This paper presents a loss minimization algorithm (LMA) based nonlinear controller for high-performance and highly efficient interior permanent magnet synchronous motor (IPMSM) drive. Among numerous LMAS a loss model-based controller (LMC) approach offers a fast response without torque pulsations. However, a difficulty in deriving the LMC lies in the complexity of the full loss model and the online motor parameter adaptation. In an effort to overcome the drawbacks of LMC, an adaptive backstepping-based nonlinear controller (ABNC) is designed to achieve high dynamic performance and at the same time some of the mechanical parameters of the motor are adapted online for the LMC. Matlab/Simulink based simulation model of the proposed LMC based nonlinear controller for IPMSM drive is built to verify the efficiency of the system. The performance of the proposed nonlinear control is also compared with the conventional i <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</inf> =0 control 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 | 0.000 | 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 it