An Improved Co-Design Method of Dynamical Controller and Asynchronous Integral-Type Event-Triggered Mechanisms
Bibliographic record
Abstract
This article addresses an improved co-design method of dynamical controller and asynchronous integral-type event-triggered mechanisms (ETMs) for a class of linear systems with external disturbances and measurement noises. First, a dynamical controller is designed for a linear disturbed plant, and two independent integral-type ETMs are synthesized to be embedded in the plant output and control input channels. Then, an augmented hybrid system is constructed, in which the integral-type ETMs in the two channels are not required to be activated synchronously and both channels are affected by their measurement noises. The proposed asynchronous integral-type event-triggered control (IT-ETC) scheme for two-fold signal transmissions can not only avoid the Zeno behavior strictly, but save more communication resources than the static event-triggered control (S-ETC) strategy. Moreover, a criterion is provided to guarantee that the hybrid system is${\mathcal {L}}_{2}$stable, and an improved co-design method is further synthesized to simultaneously obtain the design parameters of ETMs and feasible solutions of the dynamical controller. As a result, a tradeoff can be achieved between the robustness of the control system and the occupancy rate of communication resources. Compared with the S-ETC strategy, the simulation results have illustrated the effectiveness and superiority of the proposed asynchronous IT-ETC scheme.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".