A Peaking Free Time-Varying High-Gain Observer With Reduced Sensitivity to Measurement Noise
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Bibliographic record
Abstract
This study is concerned with observer design for a class of Lipschitz nonlinear systems. A high-gain observer with a straightforward structure is proposed. As opposed to the well-known high gain observers, dynamic gains obtained are used to reduce the effect of peaking. In addition, the injection term of the observer is passed through a linear filter to reduce its sensitivity to noise. It is shown that the suggested observer is peaking free with respect to the initial conditions, while achieving the input to state stability with respect to measurement noise, as a HGO. The analysis of the steady-state response also shows that the proposed observer performs better in the presence of high-frequency noise. The simulation results compare the performance of the proposed method with some existing observers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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