Intelligent speed controllers for IPM motor drives
Bibliographic record
Abstract
In this paper the comparative performances of the interior permanent magnet synchronous motor (IPMSM) drive system using proportional integral (PI) controller, proportional integral derivative (PID) controller, adaptive neural network (NN) controller, and wavelet based multiresolution proportional integral derivative (MRPID) controller are presented. In the proposed wavelet based MRPID 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 are scaled by their respective gains, and then are added together to generate the control signal. The performances of the IPMSM drive system are investigated in simulation and experiments at different dynamic operating conditions. The vector control scheme of the conventional and proposed speed controllers based IPMSM drive system is successfully implemented in real-time using the digital signal processor board ds1102 on the laboratory 1-hp IPMSM. The simulation and laboratory test results confirm the superiority of the proposed wavelet based MRPID controller over the conventional speed controllers for wide spread applications in high performance industrial motor drive systems.
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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".