Bibliographic record
Abstract
This paper will model a morphing quadrotor that is based on the works of Falanga etal [1]. It starts with the dynamics of a fixed geometry quadrotors. Getting a good understanding of the equations of motion for the system can help determine how each of the parameters can affect the system response. The use of multiple frames allows for simplification of the equation. This mixed frame approach and the use of rotation matrices significantly make the problem easier to solve. After learning about how each of the parameters affect the system, the inclusion of variable geometry means that the moment of inertia, the attitude controller gains, and the actuator controller methods require modifications. Using point masses to represent the major components of the quadrotor, estimation for the changing Moment of Inertia as a function of the morphing angle βk is calculated. Using Linear Quadratic Regulator, an adaptive attitude control allows for the changing gains to be possible mid-flight for better stability. Using the position vectors of the rotors with respect to the center of mass, the mapping for controller input, [T, Mφ, Mθ, Mψ]T, and rotor speed are determined. When comparing the simulation results with Falanga etal [1], the response follow a similar trend. Overall, the simulation response is adequate in showing the system behaviour when changing geometry mid-flight. Analysis in the variable thrust coefficient can get the simulation closer to the experimental results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".