Robust reduced-attitude control of fixed-wing UAVs using a generalized multivariable super-twisting algorithm
Bibliographic record
Abstract
Operation of fixed-wing unmanned aerial vehicles (UAVs) outside their nominal operating conditions require autopilots that can effectively compensate for highly uncertain aerodynamics, and coupled disturbances due to turbulent winds. In this paper, we propose to use a generalized multivariable super-twisting algorithm to solve the robust attitude control problem for fixed-wing UAVs. A sliding surface is designed, based on geometric methods, to perform reduced-attitude tracking while simultaneously stabilizing a turn rate based on the coordinated-turn equation. The reduced-attitude representation evolves on the unit two-sphere and is independent of the yaw/heading angle. The resulting control design is lightweight, has no singularities, and can be used with standard hierarchical control architectures for fixed-wing UAVs. The efficacy of the proposed design is demonstrated in a simulation study with highly turbulent conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".