Hybrid Fault-Tolerant Cooperative Control for a Class of Nonlinear Heterogeneous Clustered Multiagent Systems
Bibliographic record
Abstract
This study explores Hybrid Fault-Tolerant Cooperative Control (HFTCC) approach for managing actuator faults in non-linear multi-agent systems (MASs) with heterogeneous cluster dynamics. By integrating passive and active Fault-Tolerant Control (FTC), the methodology combines real-time fault monitoring using robust Unknown Input Observers (UIOs) and adaptive control adjustments via pole placement. The UIO partially decouples external disturbances and leverages an H∞-based approach to further mitigate their effects. To optimize computational efficiency in clustered MASs, each agent's observer estimates its own and neighboring agents' states and faults. Simulations validate the effectiveness of HFTCC, demonstrating a 48.76% reduction in maximum error, a 61.55% decrease in root mean square error (RMSE), and significant improvements in convergence time compared to passive FTC alone.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".