NEURAL GENERALIZED PREDICTIVE CONTROL WITH REFERENCE CONTROL MODEL FOR AN INDUCTION MOTOR DRIVE
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Bibliographic record
Abstract
In this paper the authors present a new advanced control algorithm for speed and flux tracking of an induction motor. This algorithm, called neural networks generalized predictive control (NGPC), uses a combination of artificial neural networks (ANN) and generalized predictive control (GPC) technique. The later is traditionally used for systems characterized by a slow dynamics, as in industrial process control. The NGPC algorithm is based on the use of ANN as a nonlinear prediction model of the motor. This modelling technique is done by using I/O data with no need of additional information regarding the machine parameters. The outputs of the neural predictor are the future values of the controlled variables needed by the optimization procedure, which is achieved by minimizing a cost function with a reference control model using the Newton-Raphson optimization algorithm. Simulation results show the effectiveness of the proposed control method.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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