Brain-Inspired Decision Learning for Fault-Tolerant Flight Control under Active/Passive Wing Deformations
Bibliographic record
Abstract
This paper presents an adaptive attitude control framework for unmanned aerial vehicles (UAVs) subject to random active/passive wing deformations, with rigorous stability guarantees. The proposed methodology integrates three key components: 1) a bio-inspired multi-expert fusion architecture mimicking biological control mechanisms, 2) progressive contraction theory for stability enforcement, and 3) an online performance evaluation system for control policy adaptation. The primary theoretical contribution lies in establishing a systematic control synthesis framework that formally addresses time-varying uncertainties arising from abrupt aerodynamic parameter variations and structural damage. Drawing parallels with biological nervous system’s “sensorimotor adaptation-perceptual judgment-motor skill refinement” principle, we develop a hierarchical control architecture comprising parallel expert modules, real-time performance metrics, and dynamic control law reconfiguration mechanisms. This paper provides a simulation experiment of random fault injection on a platform for longitudinal motion of fixed-wing aircraft to illustrate the performance of the proposed control scheme.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".