A Novel Neuro-Wavelet Based Self-Tuned Wavelet Controller for IPM Motor Drives
Bibliographic record
Abstract
This paper presents a hybrid neuro-wavelet scheme for on-line tuning of a wavelet-based multiresolution PID (MRPID) controller in real-time for precise speed control of an interior permanent magnet synchronous motor (IPMSM) drive system under system uncertainties. In the wavelet-based MRPID controller, the discrete wavelet transform (DWT) is used to decompose the error between actual and command speeds into different frequency components at various scales. The MRPID controller parameters are tuned by the wavelet neural network (WNN) to ensure optimum performance of the drive system. The proposed neuro-wavelet based MRPID controller is trained online with adaptive learning rates in the closed-loop vector control of the IPMSM drive system. The adaptive learning rates are derived using discrete Lyapunov stability theorem so that the convergence of the tracking error is guaranteed in the closed-loop system. The performances of the proposed hybrid controller are investigated in both simulation and experiments at different dynamic operating conditions. The complete vector control scheme incorporating the proposed self-tuning MRPID controller is successfully implemented in real-time using the ds1102 digital signal processor board for the laboratory 1-hp IPM motor. The superior performances of the proposed WNN-based self-tuning MRPID controller are also validated over fixed-gain controllers.
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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 | 0.000 |
| 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 | 0.001 | 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 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".