A Distributed Hybrid Fault-Tolerant Cooperative Control for Multi- Agent Systems with Actuator Faults and Disturbances
Bibliographic record
Abstract
This research investigates the concept of Hybrid Fault-Tolerant Cooperative Control (HFTCC), an innovative approach merging passive and active Fault-Tolerant Control (FTC). The study aims to effectively address simultaneous actuator faults within linear Multi-Agent Systems (MASs) operating within a directed communication graph and dealing with external disturbances. Using a robust Unknown Input Observer (UIO) as the initial step allows for obtaining precise insights into fault behavior, constituting the passive aspect of HFTCC. Subsequently, the active component of HFTCC is deployed to adapt the control logic, accommodating the identified fault within the system. Simultaneously, the UIO can decouple and mitigate the impact of external disturbances on the fault diagnosis process. The efficacy of the proposed robust observer performance is explained within the framework of the H∞approach. The simulation results, exemplifying the application of HFTCC for MASs, are presented and discussed. A notable decrease in error is observed when comparing the outcomes of the proposed method with those of passive FTC.
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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) | 0.000 | 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".