Event-Based Average Consensus of Disturbed MASs via Fully Distributed Sliding Mode Control
Bibliographic record
Abstract
Under undirected graph, we design the fully distributed static and dynamic event-triggered sliding mode controllers concerning the average consensus issues for single- and double-integrator multiagent systems (MASs) with perturbations. To guarantee the consensus convergence of disturbed first- and second-order MASs, two distributed sliding manifolds with respect to an odd function are first devised in this article. Second, two types of event-triggered mechanisms, i.e., a static event-triggering mechanism and a dynamic event-triggering mechanism, are established to improve the utilization efficiency of network resources and avoid the continuous communication with neighbors. In both event-triggered sliding mode control (SMC) strategies, the fully distributed event-triggered SMC laws without global information of the multiagent networks are proposed, and they can ensure the state trajectories of disturbed first- and second-order MASs to reach the average consensus. Meanwhile, the finite-time reachability of the specified sliding manifold can be guaranteed and Zeno behavior can be also averted. Third, taking advantage of the Lyapunov stability theory and SMC, sufficient conditions for the average consensus of single- and double-integrator continuous-time MASs are established. At the end, in order to show the validity of the proposed event-triggered SMC strategies, a numerical simulation and comparative study are offered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".