Tuning to Stabilize Adaptive Internal Model Controller for Periodic Disturbance Cancellation
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Bibliographic record
Abstract
This paper presents an application of adaptive internal model principle controller to an active noise cancellation problem. The algorithm is extended to handling the large phase delays that arise in active noise control problems because of the inherent transport delays. Internal model principle controllers, such as the integral action in a PI controller, require, as a necessary condition of stability, that gains are chosen to ensure negative feedback. Previously the algorithm had fixed gains which resulted in negative feedback, and stability, only for plants whose phase did not vary by more than 180 degrees. By adaptively tuning the two control gains in the feedback loop, this implicit phase requirement is eliminated. The new algorithm now requires, at a minimum, that no more than 100% uncertainty exists in the plant model. Simulations on Ben Amara's model of an acoustic duct show the effectiveness of the proposed approach.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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 it