A Low-Power Multi-High-Gain Observer Design for a Class of Nonlinear Systems
Bibliographic record
Abstract
In this paper, a low-power multi high-gain observer (low-power MHGO) is proposed where multiple low-power observers of order$2n-2$are used to improve the transient response of HGOs, as well as reducing their sensitivity to high frequency measurement noise. The MHGO methodology is applied to low power observers to aggregate the advantages of the traditional HGO, low-power HGO and MHGO. It is shown that there exists a combination of the unknown parameters for which the weighted estimated states obtained from the observer are equal to the system's states. The unknown optimal parameters are estimated using a recursive least squares approach. It is shown that the low-power MHGO reduces the peaking using a design parameter. In addition, it is shown that the observer yields the same bounds on estimation errors as a low-power HGO, in terms of the asymptotic gain between estimate error and noise frequency. A simulation example demonstrates that the proposed observer exhibits peaking reduction with reduced noise sensitivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".