Actuator fault accommodation strategy for a team of LTI multi-agent systems
Bibliographic record
Abstract
A cooperative actuator fault accommodation strategy for a team of linear time-invariant (LTI) multi-agent systems with a switching topology and directed communication network graph is studied in this paper. The faults could occur in more than one agent simultaneously and the fault severity is not needed to be precisely estimated. The proposed fault accommodation strategy is performed in two levels: the agent level fault recovery (ALFR) and the team level fault recovery (TLFR). Whenever a fault is detected, the recovery strategy is to locally recover faulty agents based on inaccurate estimates of the faults in the first step and to reconfigure the weights of the information flow graph in the second step. The proposed strategy is based on the sub-optimal solution of a bi-linear matrix inequality (BMI) and guarantees the consensus achievement of the team. The stability properties of the proposed controller and the recovery strategy are investigated based on Lyapunov analysis. The effectiveness of our proposed consensus algorithm is illustrated by performing numerical simulations for a team of ten agents and the performance of our strategy is compared with the centralized and decentralized fault recovery approaches in the literature.
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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 | 0.001 | 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".