Analysis of Urban Air Taxi Inner Loop Controller Architectures Subject to Noisy Inertial Measurement Unit Input while Immersed in Empirically-Developed Urban Airflow Disturbances
Bibliographic record
Abstract
Interest in designing and operating commercial Urban Air Taxis (UAT) around the world has grown exponentially over the last decade.One of the many challenges that are unique to UATs is the urban airflow environment in which they are envisaged to operate in.The presence of high-rise buildings creates a highly turbulent urban airflow environment which requires UATs operating within it to be equipped with highly robust inner-loop controllers to maintain steady level flight.This study investigates the effect of Angular Random Walk (ARW) noise in four grades of Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain steady level flight.Three types of control schema are examined, namely classical Proportional Integral Derivative (PID) control, Active Disturbance Rejection Control (ADRC), and Linear Active Disturbance Rejection Control (LADRC).Each control schema has a different method of overcoming ARW noise found in the IMU readings which impacts its performance.A total of three metrics are used to compare the performance of the different controllers: 1) command tracking performance; 2) control surface deflections; and 3) passenger comfort.The results of this study show promising potential for using ADRC-based control architecture in UAT inner-loop control applications by exhibting superior command tracking performance, lower control surface deflections, and increased passenger comfort compared to PID.It also provides insights to regulatory authorites and UAT manufacturers of the importance of accounting for IMU sensor noise and recommended IMU grades to be used in UAT applications.Finally, I would like to thank my family for their patience and support while I completed this work.To my parents who constantly supported my endeavor to complete this work, my wife who took on the extra load to free up my time, my brother who would make the 5-hour drive to Ottawa to help out with chores when deadlines were approaching, thank you for all you have done for me.To my four young children who never got bored of asking me whether I was done so I could play, I can now say that my work here is done and I now have time to play.It is my hope that my endeavor in completing this work serves as a source of motivation to you in the future that with dedication, determination and the right support you can achieve what may at first glance appear to be too difficult and out of reach.A special thank you to my co-supervisors Prof. Khouli and Prof. Atia for your constant support, motivation and patience when things weren't going in the right direction.Thank you to Dr.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".