Optimizing Flight Control of Unmanned Aerial Vehicles with Physics-Based Reliability Models
Bibliographic record
Abstract
The use of unmanned aerial vehicles (UAVs) is rapidly expanding across numerous industries, with a diverse range of applications. Ensuring reliable operation is crucial for safety, costs, and customer satisfaction, especially in the aviation sector. This paper presents a novel approach to optimizing flight control by incorporating a reliability-based control allocation system with physics-based reliability models. More specifically, the control allocation is based on physical estimations of reliability parameters. The reliability model incorporates a Weibull distribution reformulated to express reliability as a function of cumulated damage instead of time. The failure mechanisms of the rotor components are modeled based on physics, allowing for the calculation of cumulated damages as a function of the UAV's operation. The parameterization of the reliability and failure mechanism models is entirely based on publicly available manufacturer catalog data to ensure that the models are readily applicable to new designs with off-the-shelf components. Additionally, this approach facilitates the verification and validation of the models. The developed integrated control strategy and physics-based models have been implemented in Matlab-Simulink and applied to the case study of a coaxial quadrotor UAV for validation. When applied to the case study, the controller efficiently redistributes the control duties of rotors with a high probability of failure while maintaining the desired system response, thus increasing the operation's reliability.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".