Real-Time Implementation of a Wavelet Based Speed Controller for Induction Motor Drives
Bibliographic record
Abstract
This paper presents a real-time implementation of a wavelet-based multiresolution proportional-integral-derivate (PID) controller for accurate speed control of an induction motor drive under system uncertainties. A field oriented induction motor drive model is used to decouple the flux and torque components of the motor dynamics. The discrete wavelet transform is used to decompose the error signal between the actual and estimated speed 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 its respective gain and then added together to generate the control signal. The performance of this newly devised wavelet controller is evaluated by simulation results as well as by experimental results. The complete vector control scheme incorporating the proposed controller is successfully implemented in real-time using the DS1102 digital signal processor board for the laboratory 1-hp induction motor. In order to prove the superiority of the proposed controller over the conventional controllers, a comparison between the proposed and the conventional proportional-integral (PI) controller based systems is made at different dynamic operating conditions.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".