Modeling and control of a flying wing tailsitter unmanned aerial vehicle
Bibliographic record
Abstract
Tailsitters are a special class of fixed-wing unmanned aerial vehicle intended to bridge the gap between rotorcraft and conventional fixed-wing aircraft. These systems are able to perform aerobatic and stationary maneuvers, including vertical takeoff and landing, as well as efficient level flight. However, this flying ability brings a control challenge due to the two distinct flight regimes. During vertical maneuvers, the wings are stalled and only the thrust forces support the aircraft's weight. The rear control surfaces, called elevons, are kept effective due to the slipstream generated by the thrusters. During level flight, the aircraft flies at a substantial forward velocity which generates lift from the wings as well as control authority from the elevons. In this research, a real time simulator is developed for the full flight envelope range, based on a component breakdown method. The simulator includes a flat plate aerodynamics model which includes the effect of control surfaces deflection, a ground contact model, as well as a semi-empirical thruster model. A single quaternion-based controller is developed and implemented in this simulated environment and also tested on the real platform. The autonomous maneuvers needed for a real flight mission are demonstrated through experiments, including vertical takeoff and climb, transition to level flight, back transition, stationary flight, vertical descent and landing. The results from both simulations and flight experiments are compared and used to qualitatively evaluate the performances of the simulator.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".