Development and Implementation of a New Adaptive Intelligent Speed Controller for IPMSM Drive
Why this work is in the frame
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Bibliographic record
Abstract
In controlling nonlinear, time varying and ill defined systems artificial intelligent controllers have been proved to be superior in design and performance when compared to the conventional controllers. This paper presents a novel adaptive-network-based fuzzy inference system (ANFIS) for speed control of interior permanent magnet synchronous motor (IPMSM) drive. By utilizing a learning technique, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. The back-propagation technique is used for online tuning of ANFIS parameters in order to optimize the performance of the proposed drive. The proposed control technique also provides flux control to control the motor over a wide speed range. The complete drive has been successfully implemented in real-time using digital signal processor board DS1104. The performance of the proposed ANFIS based IPMSM drive is investigated both in simulation and experiment at different operating conditions.
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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 it