Artificial neural network based speed controller for induction motors
Bibliographic record
Abstract
This paper presents an intelligent indirect field oriented control (IFOC) technique for saturated induction motor (IM) drives in order to achieve high dynamic performance and wide operating range. The IFOC of IM drives has been traditionally carried out using linear proportional-integral (PI) controllers. As an IM is a nonlinear device due to the saturation phenomenon, conventional PI-IFOC methods provide poor performance, limited disturbance rejection capability and longer convergence time. The artificial neural network (ANN) has been widely used as an intelligent controller for nonlinear systems. ANN provides an adaptive learning ability to the controllers to better characterize the system dynamics for achieving accurate and fast responses. However, due to the iterative nature of neural networks, training of the ANN is excessively slow for saturated IM drives. In this paper, a novel neural network map (NNM) is developed to find input weights of the neurons; without the need for any recurrent training process. The proposed technique is applied on a 3-phase 4-pole 208V ¼ hp IFOC-IM drives. Both the simulation and experimental investigation have been carried out for the same motor drive, and the results are depicted and analyzed in this paper. A relative comparison between the PI controller and the proposed NNM based ANN controller indicates that the ANN mapping controller yields superior performance.
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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 | 0.000 | 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".