Unknown input observer design for a class of nonlinear systems: an LMI approach
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Bibliographic record
Abstract
A full order nonlinear unknown input observer (NUIO) for a class of Lipschitz nonlinear systems with unknown inputs is designed. A sufficient NUIO existence condition which requires solving a nonlinear matrix inequality is derived. To avoid solving the nonlinear matrix inequality directly, the existence condition is then reformulated as a new sufficient existence condition in terms of an LMI. An important advantage of this LMI based condition is that it enables us to design the proposed full order NUIO using Matlab LMI toolbox and thus makes the difficult NUIO design problem an easy task for the considered class of nonlinear systems. The new sufficient condition, when applied to linear systems, is also necessary. An example is given to show how to use the LMI approach to design the proposed NUIO, and simulation results are presented.
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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 | 0.000 | 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 | 0.000 | 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