Tuning an Active Disturbance Rejection Control-Based Autopilot for Multirotor-Based UAM Operations in Turbulent Winds
Bibliographic record
Abstract
Urban Air Mobility (UAM) is a burgeoning area in the aviation sector. It will bring increased movement of people and goods to the urban environment, however not without challenges. Winds in cities, referred to as Urban Airflow, have several defining characteristics of which turbulence is only one aspect. Turbulence levels in cities can exceed levels typically found at airports and will pose a challenge for UAM. A successful implementation of UAM in any city will require that the limitations associated with operations in turbulent wind conditions be understood. The small multi-rotor based aircraft that are currently being operated make use of a flight controller to ensure stable flight. This paper focuses on tuning a flight controller for operations in urban airflow conditions. An Active Disturbance Rejection Control (ADRC) based flight controller for a small multi-rotor is designed and tuned by Particle Swarm Optimization (PSO). Few investigations into the performance of ADRC in the aforementioned conditions have been conducted to date. The ADRC-based autopilot is tuned and flown in simulated urban airflow conditions that are based on empirical airflow measurements, and the performance is evaluated and compared to that of classical Proportional-Integral-Derivative (PID) control. Findings indicate that the ADRC autopilot outperforms PID control in urban airflow conditions. Despite the complexity of tuning ADRC parameters, this paper demonstrates that PSO can effectively address this challenge, showcasing the enhanced performance of the ADRC flight controller in urban environments.
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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) | 0.000 | 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) | 0.000 | 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".