Full-Envelope Flight Control for Compound Vertical Takeoff and Landing Aircraft
Bibliographic record
Abstract
This paper presents a flight control design for compound vertical takeoff and landing (VTOL) vehicles. With their multitude of degrees of controllability as well as the significant variations in their flight characteristics, VTOL vehicles present challenges when it comes to designing their flight control system, especially for the transition phase where the vehicle transitions between near-hovering and high-speed wing-borne flights. This work extends previous research on the design of unified and generic control laws that can be applied to a broad class of vehicles such as hovering vehicles and fixed-wing aircraft. This paper exploits this unifying property and presents an extension for the case of compound VTOL vehicles. The proposed control approach consists of nonlinear geometric control laws that are continuously applicable over the entire flight envelope, excluding the use of switching policies between different control algorithms. A transition strategy consisting of a sequence of high-level set points is associated with the flight control laws; it is defined with respect to flight envelope limitations and is applied in this work to a commercially available compound unmanned aerial vehicle. The control algorithms are implemented on a Pixhawk controller; they are evaluated via hardware-in-the-loop simulations and finally validated in a flight experiment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".