SMO-Based Distributed Tracking Control for Linear MASs With Event-Triggering Communication
Bibliographic record
Abstract
This article is devoted to the robust tracking control issue for leader-following linear multiagent systems (MASs) in the case of unavailable states, external interferences, and limited network bandwidth. First, a distributed sliding-mode observer (SMO), including neighbor output information, which can effectively cope with external interferences in the closed-loop system and estimate the unmeasurable states of linear MASs, is established. Second, in order to prevent continuous communication and resize the activated interval, a distributed dynamic triggering transmission mechanism based on the SMO state is constructed. Then, the bounded consensus tracking performance of disturbed linear MASs with unavailable states is well realized by devising a distributed robust control protocol in terms of the SMO state and event-triggered communication mechanism. By employing the Lyapunov stability theory and Riccati equation, some ample conditions are deduced to guarantee the leader-following bound consensus for linear MASs subject to perturbations and unmeasured states. Finally, to further validate the validity of the SMO-based event-triggered communication control strategy, an emulation example is provided.
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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 | 0.000 |
| 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) | 0.001 | 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".