Finite-Frequency Fault Estimation and Adaptive Event-Triggered Fault-Tolerant Consensus for LPV Multiagent Systems
Bibliographic record
Abstract
This article investigates the problem of finite-frequency fault estimation (FE) and adaptive event-triggered fault-tolerant consensus for linear parameter-varying multiagent systems. A polytopic parameter-varying framework is introduced to represent the dynamics of each agent with internal model perturbation and parameter uncertainties. In order to reduce the conservatism brought by full-frequency domain approaches, the finite-frequency technique is employed to design a FE observer that can estimate the magnitude of faults. To eliminate/reduce the impact of faults on system performance, an adaptive event-triggered fault-tolerant consensus controller is then developed, which adjusts the consensus protocol based on the FE information. With the developed distributed fault-tolerant protocol and adaptive event-triggered control scheme, the agents can reach consensus in the presence of system faults and the transmission of unnecessary information in the control channels is avoided. The proposed triggering scheme offers certain advantages over existing results in balancing desired consensus performance and improving network utilization. By constructing a parameter-dependent Lyapunov function, a sufficient condition for designing the consensus controller gain and the adjustment matrix can be derived in the form of linear matrix inequality. Finally, two simulation examples are included to illustrate the effectiveness of the obtained theoretical 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 | 0.003 |
| 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".