IMPROVED LQR-BASED CONTROL APPROACH FOR HIGH PERFORMANCE INDUCTION MOTOR DRIVES
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Bibliographic record
Abstract
An improved LQR-based control approach with an aim to high performance control systems for induction motor (IM) drives is presented. The proposed algorithm incorporates dynamic separable first order filters with appropriate time constants and integral actions to uncouple the flux and the torque/speed of an IM and provide the same performance as achieved by a separately excited DC machine. The proposed approach leads to optimal feedback gains for the control loops and ensures a decoupling between the system outputs while guaranteeing reduced stationary errors and avoiding inadmissible amplitude of the control signals. Simplicity of the overall scheme, minimization of the required energy and the elimination of the need for gain tuning required by the classical Field Oriented Control (FOC) are the main positive features of the proposed approach. The performances of the proposed approach are analysed in this paper, then compared to those obtained by the classical FOC.
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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 | 0.000 |
| 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