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Analysis of an Urban Air Taxi Inner Loop Controller with Noisy IMUs in an Urban Airflow Environment

2023· article· en· W4376606072 on OpenAlexaff
Tariq Maksoud, Fidel Khouli, M. R. Atia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsSetpointPID controllerAirflowInertial measurement unitControl theory (sociology)Controller (irrigation)Computer scienceAccelerationControl systemInner loopSimulationEngineeringControl engineeringControl (management)Artificial intelligencePhysicsTemperature control

Abstract

fetched live from OpenAlex

Interest in operating commercial Urban Air Taxis (UAT) around the world has been growing rapidly over the last few years. One of the many challenges in designing aircraft suitable for operating in a turbulent urban airflow environment is to design a robust inner loop flight controller. This study investigates the effect of filtered Angular Random Walk (ARW) error found in Inertial Measurement Units (IMU) on the inner loop flight controller's ability to maintain stable, wings level, horizontal flight, while not causing noticeable discomfort to passengers and respecting the limits of authority of the aircraft's control surfaces in a representative urban airflow environment. The performance of two controller architectures were investigated: classical Proportional, Integral, Derivative (PID) control scheme as well as Linear Active Disturbance Rejection Control (LADRC) control scheme. The conclusion of this study provides recommendations on a minimum threshold of IMU sensor grades and general considersations that would be useful to the controller designer. The findings are demonstrated by observing the vertical acceleration, <tex>$n_{z}$</tex>, angular rate setpoint tracking performance, and control surface deflections.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.188
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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