A New Wavelet-based Speed Controller for Induction Motor Drives
Bibliographic record
Abstract
This article presents a novel speed controller based on multi-resolution decomposition analysis of the discrete wavelet transform for vector-controlled induction motor drives. The field-oriented control principle is used to decouple the flux and torque components of the induction motor dynamics. In the proposed controller, the discrete wavelet transform is used to decompose the speed error between the actual and command speeds at various scales. The control signal is generated using the wavelet-transformed coefficients of speed error of different scales. It has been found that these coefficients can represent the cumulative effect of motor drive uncertainties such as parameter variations, measurement noise, frictional variation, and external torque disturbances. The performance of this controller is evaluated in both simulation and experiments. The complete vector-control scheme incorporating the proposed controller is successfully implemented in real time using the ds1102 digital signal processor board (dSPACE, GmbH, Paderborn, Germany) for the laboratory 1-hp induction motor. The experimental results validate the robustness and, hence, justify the applicability of the proposed controller for the induction motor drive to be used in high-performance drive applications. In order to prove the superiority of the proposed controller, a comparison between the proposed and the conventional proportional-integral and proportional-integral-derivative 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".