Design of switched high-gain observer for nonlinear systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a switched high-gain observer with the desired convergence time for nonlinear systems. The proposed approach is based on a switching structure that plays an essential role, resulting in desired convergence and avoiding the observer's states' singularity. The main feature of this paper is that the observer's state converges to the actual state within the desired convergence time, where the convergence time is invariant with respect to the initial errors of the system. Moreover, the convergence time can be chosen at the will of the designer. The proposed method's results involve the high-gain principle and recent advancements in fixed-time observers. Further, the system's gain varies linearly (rather than square) with the order of the system. Using the Lyapunov theorem, the stability analysis of the proposed approach is investigated. Finally, two examples, (i) the Van der Pol oscillator circuit and (ii) Genesio–Tesi chaotic system demonstrate the efficacy 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 | 0.000 |
| 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 | 0.001 | 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