Event‐triggered predictor‐based control of distributed parameter systems
Bibliographic record
Abstract
Abstract This paper deals with the point control problem for a class of distributed parameter systems with time varying delay induced by the network. To eliminate the effect of time delay, a predictor with the time‐varying gain is designed to predict the state based on the sampled data. Meanwhile, the prediction error vanishes exponentially with the desired decay rate. To lighten greatly network loads and effectively improve the utilisation of the resource, an event‐triggered communication scheme is proposed to determine the transmitting of necessary sampled data. Then, based on the point feedback controller, the exponential stability condition of the distributed parameter system with the event‐triggered scheme is derived in the framework of linear matrix inequality. Furthermore, the feedback gain is given in this paper by using the Lyapunov–Krasovskii method where a novel Lyapunov–Krasovskii functional is constructed. The event‐triggered time interval is presented to show the number of maximum allowable packet loss. Finally, an example of a food web model is given to illustrate the effectiveness 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".