Cooperative actuator fault accommodation in formation flight of unmanned vehicles using relative measurements
Bibliographic record
Abstract
In this article, the cooperative fault accommodation in formation flight of unmanned vehicles is investigated through a hierarchical framework. Three levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery (FLFR) and a high-level (HL). In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is introduced at the HL hierarchy to identify a partially low-level (LL) recovered vehicle due to inaccuracy in the fault estimate which results in violating the error specification of the formation mission. The HL supervisor then activates the FLFR module to compensate for the performance degradations of the partially LL recovered vehicle at the expense of the other healthy vehicles. Both centralised and decentralised control approaches are developed for our proposed cooperative fault recovery technique. A robust H ∞ controller is designed in which the parameters of the controller are adjusted to accommodate for the partially LL-recovered vehicle by enforcing that the other healthy vehicles allocate more control effort to compensate for the performance degradations of the faulty vehicle. Numerical simulations for a formation flight of five satellites are provided in the deep space, which do indeed confirm the validity and effectiveness of our proposed analytical work.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".