Implementation of a New Wavelet Controller for Interior Permanent-Magnet Motor Drives
Bibliographic record
Abstract
This paper presents the real-time implementation of a novel wavelet-based multiresolution proportional-integral-derivative (PID) controller for the accurate speed control of the interior permanent-magnet (IPM) synchronous motor drives under system uncertainties. In the proposed wavelet-based PID controller, the discrete wavelet transform is used to decompose the error between actual and command speeds into different frequency components at various scales. The wavelet-transformed coefficients of different scales, which represent many underlying phenomena such as process dynamics, measurement noise, and effects of external disturbances, are scaled by their respective gains and, then, are added together to generate the control signal. The performance of this newly devised wavelet controller is evaluated in both simulation and experiments. The complete vector control scheme incorporating the proposed wavelet controller is successfully implemented in real time using the ds1102 digital signal processor board for the laboratory 1-hp IPM motor. In order to prove the superiority of the proposed controller over conventional controllers, a comparison between the proposed and fixed-gain controller-based systems is made at different dynamic operating conditions.
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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 | 0.001 |
| 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".