Automatic autopilot tuning framework using genetic algorithms and system identification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a comprehensive framework for offline optimization of tuning parameters in unmanned aerial vehicle (UAV) flight controllers. The framework uses system identification to create a simplified flight dynamics model, followed by control law matching to ensure the simulated controller's output closely replicates real-world autopilot commands. The optimization phase employs genetic algorithms to tune parameters based on a defined cost function that incorporates performance requirements. Each stage, from flight dynamics model development to optimization, is validated to ensure enhanced controller performance. Finally, real-world flight tests confirm the effectiveness of the optimized controller, demonstrating the validity of the proposed framework for autopilot tuning optimization. • Genetic Algorithms optimize UAV autopilot tuning for enhanced flight performance. • System identification simplifies UAV flight dynamics model creation for tuning. • Real-world flight tests validate the proposed autopilot tuning framework. • The framework adapts to various UAV controllers and vehicle configurations. • Optimized controller parameters improve stability and performance in UAVs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it