Active Disturbance Rejection Control of an Urban Air Mobility Vehicle: A Computational Investigation
Bibliographic record
Abstract
Advanced and Urban Air Mobility are rapidly expanding new areas of development of the aviation sector. Operations involving small aerial vehicles operating in the urban environment have already been conducted. These type of operations are expected to encounter turbulent winds due to urban structures that could affect their performance and limit safe operation. This paper presents the development of a simulation to predict the flight performance of a quadrotor operating in representative urban airflow conditions while using PID and Active Disturbance Rejection Control (ADRC) flight controllers. The performances of the two controllers are compared for different cases with one out-performing the other depending on the case. It was found that when ADRC is applied without appropriate tuning methodologies it will not outperform PID. However, for one case involving altitude control at a stationary point, ADRC did outperform PID. This is likely due to better chosen ADRC gain values and points to the potential suitability of ADRC for control in turbulent wind conditions. This work lays the groundwork for future investigations to assess controller performance in urban airflows and provides insight on which conditions are more and less challenging for different controller types.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".