Optimal speed tracking control of induction motor using artificial intelligence techniques
Bibliographic record
Abstract
This paper presents a novel neural network based control architecture which use on line training to identify and control the nonlinear induction motor. The aim of this control is to force the shaft speed to follow a prescribed trajectory. The architecture incorporates two artificial neural networks and a fuzzy logic controller. Accepting that not all elements of the state are measurable, the first ANN is used as observer to give an estimate of the state. A state space description is applied, and the trained nonlinear innovation state space model of the motor is used. Since the motor is nonlinear, and since the observer, as well as, the controller (second ANN) are trained based on optimal criteria, the method is named non linear quadratic Gaussian. A fuzzy logic controller is used to provide an inner loop inspired by conventional vector control strategy. Simulated results are presented to validate the proposed architecture showing that speed control is stable, rapid to stabilize, and insensitive to parameter uncertainty and load disturbance.
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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".