Physics-Based Reliability Modeling for Control Applications: Adaptative Control Allocation
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly utilized across various industries, necessitating high reliability to ensure safety and reduce operational costs. This paper introduces a systematic methodology for physics-based reliability modeling tailored for UAV control applications, focusing on adaptive control allocation to optimize reliability. The study addresses the limitations of conventional degradation-independent behavior factor-based models, which often rely on inaccurate degradation models due to the lack of parameterization data sources and methods. By replacing time with stress-derived variables in the reliability function, this approach enables the combination of any time-dependent reliability functions with any stress-life relationships, allowing for real-time physics-based reliability assessments. The approach is demonstrated through the development and application of models for electronic speed controllers within a hexarotor UAV, using a virtual prototype for flight simulation. Simulation results reveal that physics-based models improve reliability prediction accuracy compared to conventional proportional hazard models, particularly due to their reliance on published and manufacturer’s data for parameterization. The paper concludes by highlighting the need for future research to simplify the integration of stress factor online measurements, addressing the complexity and data requirements inherent in physics-based models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 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".