Controllability Assessment and Fault-Tolerant Sizing of UAVs Under Effector Failures
Bibliographic record
Abstract
This article presents a methodology for analyzing the controllability and fault-tolerant sizing of fixed-wing and hybrid fixed-wing vertical landing and takeoff (FW-VTOL) unmanned aerial vehicles (UAVs). Building upon previous research focused on multirotor UAVs, the study introduces a new control authority index, redefined dynamics models, and enhanced control allocation techniques to address rotor failures and lock-in-place failures of control surfaces. The methodology considers effector limitations and the unidirectional drag condition for control surface deflection angles.These advancements are combined to assess the linear controllability and accomplish fault-tolerant sizing of UAVs. By applying the proposed methodology to a case study, the linear controllability of fixed-wing and hybrid FW-VTOL UAVs is evaluated in various failure scenarios. The results demonstrate that the hybrid FW-VTOL concept exhibits higher fault tolerance capability due to the presence of VTOL rotors that can compensate for control surface failures. The study emphasizes the importance of oversizing VTOL rotors to ensure sufficient control authority in double failure scenarios, revising the conventional assumption that VTOL system sizing primarily relies on takeoff analysis.The proposed advancements are relevant to future UAV designs intended for fault-tolerant applications such as medical equipment transport and air taxis.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".