An Intelligent Hybrid Control System using ANFIS-Optimization for Scalar Control of an Induction Motor
Bibliographic record
Abstract
Recently, the three-phase induction motor (IM) has been widely used in AC motor drives and in industrial applications.The IM suffers from the accuracy of controlling the speed when it operates at different loads; this problem attracts the attention of many researchers in this field.This paper presents an Intelligent Hybrid Control System using ANFIS (Adaptive Neuro-Fuzzy Inference System)-Optimization for Scalar Control (SC) of an Induction Motor.In order to obtain optimum performance of the motor and to decrease the Total Harmonics Distortion (THD) of the motor current a Voltage Source Inverter (VSI) based on the Pulse Width Modulation technique (PWM) is used to drive the motor.To improve the speed response, accuracy and the motor's performance, an improved hybrid control system involves an optimization control method in addition to an Adaptive Neuro-Fuzzy Inference System used to adjust the amplitude and the Modulation Index (MI) of the reference signal.The proposed hybrid system improves the transient stability of motor speed and reaches a steady state much faster than the traditional controller.The Matlab-Simulink results proved the remarkable effectiveness of the proposed controller when comparing the results with two other controllers, the usual PI controller and the optimization controller.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".