Control Allocation with Physics-Based Reliability Models for Multirotor UAVs
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2371.vid This paper introduces a control method to optimize the reliability of underactuated multirotor unmanned aerial vehicles (UAV) while still meeting desired performance goals. Reliability is key in safety, costs, and customer satisfaction, especially in aeronautics. It is, therefore, essential to address this aspect in the design process. Previous work demonstrated the possibility of optimizing the control of multirotor UAVs as a function of reliability. For efficiency and simplicity, the models used in this previous work approximate the component reduction in reliability without represent-ing the physical phenomena causing the degradation of the components. Thus, the ac-tual reliability can significantly diverge from the predictions. This paper combines a weighted control allocation driven by physics-based health models of the rotors to optimize online reliability with more accurate reliability predictions and preserve flight performance. An octocopter case study qualitatively validates the developed methodology and models as proof of concept. Simulation shows that the control duties of mo-tors with high failure rates are redistributed while maintaining the desired system response. The proposed control optimization method applies to all types of underactuated multirotor UAVs and can contribute to the emergence of highly reliable applications.
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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) | 0.000 | 0.001 |
| 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) | 0.002 | 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".