A New Event-Triggered Control Scheme for Stochastic Systems
Bibliographic record
Abstract
This article studies event-triggered control of stochastic linear discrete-time systems with discounted quadratic cost functions. A new dynamic event-triggering condition is proposed, which has simultaneously stochastic and deterministic features. The designed event-triggered control system ensures the control performance to be within a desirable level relative to that using periodic time-triggered control, while discarding the unnecessary transmissions. By adjusting the parameters, the proposed event-triggering condition can be reduced to some existing ones in the literature. It is shown that the three features (dynamic, stochastic, and deterministic) are all helpful to further increase the average interevent times. Then, the criteria in terms of the parameters are presented to ensure mean-square stability of the closed-loop systems. Moreover, an improved version of the proposed event-triggering condition is given to enlarge the minimum interevent times. Finally, numerical simulations are given to illustrate the efficiency and feasibility of the proposed results.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".