Bibliographic record
Abstract
<p>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.</p>
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 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".