Event-triggered stabilization of disturbed linear systems over digital\n channels
Bibliographic record
Abstract
We present an event-triggered control strategy for stabilizing a scalar,\ncontinuous-time, time-invariant, linear system over a digital communication\nchannel having bounded delay, and in the presence of bounded system\ndisturbance. We propose an encoding-decoding scheme, and determine lower bounds\non the packet size and on the information transmission rate which are\nsufficient for stabilization. We show that for small values of the delay, the\ntiming information implicit in the triggering events is enough to stabilize the\nsystem with any positive rate. In contrast, when the delay increases beyond a\ncritical threshold, the timing information alone is not enough to stabilize the\nsystem and the transmission rate begins to increase. Finally, large values of\nthe delay require transmission rates higher than what prescribed by the classic\ndata-rate theorem. The results are numerically validated using a linearized\nmodel of an inverted pendulum.\n
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".