Modelling, Flight Dynamics and Inner-Loop Control of an Urban Air Mobility Vehicle Subject to Empirically-Developed Urban Airflow Disturbances
Bibliographic record
Abstract
Advanced Air Mobility in general, and Urban Air Mobility in particular, are expected to play a significant role in improving transportation of people and goods in increasingly growing urban centres while contributing to reduction in emissions and reaching the goal of sustainable urban growth.The goal of this work was to investigate the performance of a novel controller as an autopilot for a generic aircraft operating in urban airflow.In this investigation, the extraction of geometric, inertial and aerodynamic properties of a generic urban air taxi, similar in design to the Bell Nexus 4EX, was carried out.Subsequently the properties were used as a basis for the development of a linearized flight dynamics numerical model of the aircraft.Flights about a trimmed condition at cruising speed were simulated including cases where the urban air taxi was immersed in empirically-developed urban airflow disturbances.The open-loop response of the urban air taxi was assessed and classical proportional-integral-derivative and Active Disturbance Rejection Control inner-loop controllers were developed.The performance of the controllers in presence of the aforementioned urban airflow disturbance were then examined and compared.Active Disturbance Rejection Control was shown to perform similarly to proportional-integral-derivative control in the flight of the aircraft immersed in a representative urban airflow environment.Future investigations were recommended to increase the model fidelity to understand how unsteady aerodynamics would affect the aircraft motion and that real world considerations be included to further test Active Disturbance Rejection Control.
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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) | 0.000 | 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) | 0.001 | 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".