Adaptive Cancellation of Sinusoidal Disturbances in Narrow Bands With Unknown Frequencies
Bibliographic record
Abstract
Most existing techniques cancel the entire disturbance signal, even when it includes components that should be preserved. In contrast, this paper presents an adaptive control algorithm that selectively rejects only the sinusoidal disturbance while preserving non-sinusoidal signals within a narrow frequency band. The controller is designed based on the Internal Model Principle (IMP) and combines a Two-Degree-of-Freedom (2-DOF) structure with internal dynamics for sinusoidal cancellation. A low-pass filter with a notch is used to guide the tuning process. The controller parameters are updated online by matching the closed-loop transfer function to that of the desired filter. Real tremor data, which includes both sinusoidal (tremor) and non-sinusoidal (voluntary motion) components, is used to validate the approach. Both simulation and experimental results demonstrate the algorithm’s effectiveness in real-time rejection of tremor signals with unknown frequencies while preserving voluntary motion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".