Observer design for linear multi-rate sampled-data systems
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Bibliographic record
Abstract
This paper addresses observer design for linear systems with multi-rate sampled output measurements. The sensors are assumed to be asynchronous and to have uncertain nonuniform sampling intervals. The contributions of this paper are twofold. Given the maximum allowable sampling period (MASP) for each sensor, the main contribution of the paper is to propose sufficient Krasovskii-based conditions for design of linear observers. The designed observers guarantee exponential convergence of the estimation error to the origin. Most importantly, the sufficient conditions are cast as a set of linear matrix inequalities (LMIs) that can be solved efficiently. As a second contribution, given an observer gain, the problem of finding MASPs that guarantee exponential stability of the estimation error is also formulated as a convex optimization program in terms of LMIs. The theorems are applied to a unicycle path following example.
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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 | 0.000 | 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