Modelling and Multivariable Control Techniques for Small Coaxial Helicopters
Bibliographic record
Abstract
ight of small coaxial helicopters ( < 70cm rotor diameter) poses signicant challenges in terms of comprehensive yet computationally feasible modeling and control. The coaxial platform provides several advantages at small scales in terms of size, footprint, eciency and stability. This study compares techniques used for the modeling and control of such an aircraft in order to identify a viable control design for an experimental platform. Models of the various thrust, servo and motor dynamics are presented, and the delity of the model is assessed. In terms of control techniques, linear methods such as PID, LQR and H1 mixed synthesis are presented. PID control is generally found to be ineective in most cases including trajectory tracking and disturbance rejection. LQR and H1 control techniques outperform PID in this regard and provide respectable results. Furthermore, the H1 control scheme is especially eective in achieving tighter trajectories.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".