Validation of Reliability-based Flight Control Optimization for UAVs
Bibliographic record
Abstract
This paper presents an innovative approach to optimize flight control of unmanned aerial vehicles (UAVs) by incorporating a reliability-based control allocation system with physics-based reliability models. These models calculate the cumulative damage of rotor components based on UAV operation, using a Weibull distribution reformulated to express reliability as a function of cumulated damage. The proposed system uses adaptive control to redistribute control duties of rotors with high failure probability while maintaining the desired system response. Reliability and failure mechanism models are parameterized based on publicly available manufacturer catalog data, ensuring applicability to new designs with off-the-shelf components. Building upon prior studies, this paper incorporates physics-based reliability models for an additional critical component, the battery. A series of tests using a virtual prototype were conducted, covering a range of flight maneuvers and setups. The results confirm the efficacy of the reliability optimization and show that this method does not compromise UAV performance. The results also underscore the significance of UAV design, system architecture, and the precision of reliability model parametrization in realizing the full benefits of the reliability optimization.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".