Prediction of the Flight Dynamics of Maneuvering Multirotor Aircraft
Bibliographic record
Abstract
A simulation environment is presented that predicts the flight trajectory of a maneuvering multirotor aircraft using a purely physics‐based approach without the need for a priori flight test data. The flight dynamics model determines the motion of the aircraft based on the total loads and commanded motor speeds. The aerodynamic loads of the rotors are predicted using a modified blade element momentum theory (BEMT)–based approach that considers nonuniform inflow conditions at the rotor discs. In addition, the aerodynamic loads of the remaining aircraft components are estimated using a load decomposition. Flight test data of an AscTec Pelican quadcopter were used to evaluate the prediction quality by comparing it with the vehicle tracks recorded in flight tests. As the flight changes from hover, the present approach shows significant prediction improvements over a simple KΩ2 approach. Specifically, when comparing the number of successful prediction timesteps into the future, the BEMT‐based approach showed, on average, 44.4% longer successful predicting for positional velocities and 85.3% longer for predicting body rates. In addition to its numerical accuracy, the simulation environment is computationally efficient and thus ideal for design studies of flight controllers. The codes associated with the simulation environment are open source.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".