Nonlinear Vector-Projection Control for Agile Fixed-Wing Unmanned Aerial Vehicles
Bibliographic record
Abstract
Agile fixed-wing aircraft integrate the efficient, high-speed capabilities of conventional fixed-wing platforms with the extreme maneuverability of rotorcraft. This work presents a nonlinear control strategy that harnesses these capabilities to enable autonomous flight through aggressive, time-constrained, three-dimensional trajectories. The cascaded control structure consists of two parts; an inner attitude control loop developed on the Special Orthornormal group that avoids singularities commonly associated with other parametrizations, and an outer position control loop that jointly determines the thrust command and attitude references by implementing a novel vector-projection algorithm. The objective of the algorithm is to decouple roll from the reference attitude to ensure that thrust and lift forces can always be pointed such that position errors converge to zero. The proposed control system represents a single, unified solution that remains effective throughout the aircraft's flight envelope, including aerobatic operation. Controller performance is verified through simulations and experimental flight tests; results show the unified control scheme is capable of performing a wide range of operations that would normally require multiple, single-purpose controllers, and their associated switching logic.
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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".