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Record W2029569070 · doi:10.1115/detc2011-48806

Fault Tolerant Control Applied to a Quadrotor Unmanned Helicopter

2011· article· en· W2029569070 on OpenAlexafffund
Tong Li, Youmin Zhang, Brandon W. Gordon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsActuatorPropellerControl theory (sociology)Fault toleranceFault (geology)EngineeringController (irrigation)Attitude controlSliding mode controlFlight control surfacesControl engineeringControl systemComputer scienceControl (management)AerodynamicsAerospace engineeringMarine engineeringArtificial intelligenceReliability engineering

Abstract

fetched live from OpenAlex

In this paper, two sliding mode based fault tolerant control (SM-FTC) strategies are designed, implemented and flight-tested in a physical quadrotor unmanned helicopter under the propeller damage and actuator fault conditions. Sliding model control (SMC) is well known for its capability of handling uncertainty and is expected to be a robust controller. Based on the concept of sliding mode control, both passive and active fault tolerant controls have been designed and experimentally tested on a quadrotor UAV (unmanned aerial vehicle) test-bed, known as Qball-X4, available at Concordia University in the presence of actuator faults and propeller damages. These two types of controllers are carried out and compared through theoretical analysis, simulation, and experimental flight tests on the quadrotor UAV system. Good control performance has been achieved in the presence of actuator faults and propeller damages.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.019
GPT teacher head0.192
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2011
Admission routes2
Has abstractyes

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