Cooperative actuator fault accommodation in formation flight of unmanned vehicles using absolute measurements
Bibliographic record
Abstract
In this study, the problem of cooperative fault accommodation in formation flight of unmanned vehicles represented by linear time-invariant models that are subject to loss-of-effectiveness actuator faults is investigated through a hierarchical framework. Three hierarchical levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery and a high-level supervisor. In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is then introduced at the high-level to identify a ‘partially low-level recovered’ vehicle because of inaccuracy in the fault severity estimate that results in violating the ‘error specification’ of the formation mission. The high-level supervisor then activates the formation-level fault recovery module to compensate for the resulting performance degradations of the partially low-level (LL) recovered vehicle at the expense of other healthy vehicles. The fault is accommodated by reconfiguring the formation structure through the novel notion of the weighted absolute measurement formation digraph, activating a robust controller for the partially LL recovered vehicle, and imposing a constraint on the desired input signals. Numerical simulations for a formation flight of five satellites in the planetary orbital environment are presented to confirm the validity and effectiveness of the 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) | 0.001 | 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) | 0.000 | 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".