Noise Sensitivity Reduction in Low-power Multi High Gain Observers Using Low-pass Filters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Low-power multi high gain observers (LP MHGO) are proven to be effective in reducing the peaking of state estimation of nonlinear systems to an arbitrarily small magnitude. Moreover, they reduce the sensitivity of estimates to measurement noise. They also relax the numerical implementation problem of high gain observers by using gains powered up to the order of 2 instead of n. In this paper, we aim to further reduce the noise sensitivity of these observers by employing low-pass filters in the observer dynamics. The main results establish the convergence of the estimation error to zero with an arbitrarily small decay rate in the absence of noise, as well as an input to state stability feature when the noise is present. We also demonstrate in the linear case that the proposed observer improves the upper bound on the estimates. Simulation results compare the performance of the proposed observer with similar works and show the effectiveness of the proposed method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 |
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