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Record W2081496485 · doi:10.1115/detc2011-48755

Fault Tolerant Control of a Quadrotor Helicopter Using Model Reference Adaptive Control

2011· article· en· W2081496485 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Control Systems Optimization
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)ActuatorTrajectoryFault (geology)Fault toleranceController (irrigation)Linear-quadratic regulatorAttitude controlAdaptive controlControl engineeringEngineeringComputer scienceControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a useful approach to Fault Tolerant Control (FTC) based on the Model Reference Adaptive Control (MRAC) technique with application to a quadrotor helicopter Unmanned Aerial Vehicle (UAV) in hovering as well as trajectory tracking flight in order to control and keep the desired height and trajectory of the quadrotor helicopter in both normal conditions and in the presence of faults in one or more actuators. A Linear Quadratic Regulator (LQR) controller is used in cooperation with the MRAC to control the pitch and roll attitude of the helicopter. Three cases of fault are considered: 1) simulated fault in all the four actuators; 2) simulated fault in back and right motors; 3) a physical damage of 23% of one of the four propellers during autonomous flight. It can be seen from the test results that under the faulty and damage conditions MRAC controller provided a good response of the quadrotor UAV and result in safe landings of the quadrotor.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.225
Teacher spread0.180 · 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

Quick stats

Citations5
Published2011
Admission routes2
Has abstractyes

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