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Record W2315522507 · doi:10.2514/6.2011-6716

Fault/Damage Tolerant Control of a Quadrotor Helicopter UAV using Model Reference Adaptive Control and Gain-Scheduled PID

2011· article· en· W2315522507 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

VenueAIAA Guidance, Navigation, and Control Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
FundersConcordia University
KeywordsControl theory (sociology)PID controllerFault toleranceComputer scienceControl engineeringAdaptive controlControl (management)Reference modelEngineeringTemperature controlArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, two useful approaches to Fault Tolerant Control (FTC) for a quadrotor helicopter Unmanned Aerial Vehicle (UAV) in the presence of fault(s) in one or more actuators during flight have been investigated and experimentally tested based on a Model Reference Adaptive Control (MRAC) and a Gain-Scheduled Proportional-IntegralDerivative (GS-PID) control. A Linear Quadratic Regulator (LQR) controller is used in cooperation with the MRAC and the GS-PID to control the pitch and roll attitudes of the helicopter. Unlike the MRAC, the GS-PID is used only to control the helicopter in height control mode. MRAC is used to control the helicopter in both height control as well as trajectory control. For damage tolerant control the MRAC is evaluated based on partial damage of one of propellers during flight. Finally, the experimental flight testing results of both controllers are presented for the fault tolerant control performance comparison in the presence of actuator faults in the quadrotor UAV.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.238
Teacher spread0.196 · 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