Nonlinear Predictive Control with Disturbance Observer for Induction Motor Drive
Why this work is in the frame
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Bibliographic record
Abstract
A nonlinear predictive control (NPC) law with a disturbance observer is presented. It is applied to induction motor in order to track speed and flux profiles. The prediction defined on finite horizon is carried out via Taylor series expansion. The load torque is considered as an unknown disturbance and it is estimated by a nonlinear observer. Using the nonlinear predictive control has simplified the structure of the dynamic observer. The combination of the predictive controller and the observer works as a nonlinear controller with an integral action. The stability of the whole system can be achieved by simple design parameters. The simulations show very satisfactory performance of the proposed controller for trajectories tracking, the robustness to parameters variations and the disturbance rejection are successfully achieved
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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) | 0.000 | 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 it