Switching event‐triggering mechanisms for integral input‐to‐state stable nonlinear systems
Bibliographic record
Abstract
Abstract This article studies the event‐triggered control problem for nonlinear systems that are merely integral input‐to‐state stable (iISS) with respect to measurement errors and external inputs. By using the iISS Lyapunov function, which comes from a necessary and sufficient condition on iISS, an integral‐based event‐triggering mechanism is introduced to compensate the effects of measurement errors. Under some assumptions on the gains of the iISS Lyapunov function, both the closed‐loop iISS and Zeno‐freeness are proved when the external inputs are measurable. In the presence of unknown disturbances, a novel switching event‐triggering mechanism is designed based on the relationship between the transmitted state and some threshold constant. Subsequently, the corresponding lower bound of inter‐event times is given explicitly to ensure Zeno‐freeness. It is shown that the proposed switching mechanism can be applicable to the nonlinear systems where some existing event‐triggering mechanisms are invalid in avoiding Zeno behavior. Finally, numerical simulations are provided to illustrate the efficiency and feasibility of the obtained 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 | 0.002 |
| 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 | 0.001 |
| 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".