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Record W1822531551

Fault tolerant control using linear quadratic technique against actuator faults in a UAV

2013· article· en· W1822531551 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.

Bibliographic record

VenueChinese Control Conference · 2013
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)Fault toleranceActuatorWeightingAlgebraic Riccati equationRiccati equationController (irrigation)Linear-quadratic regulatorFault (geology)Control engineeringComputer scienceStability (learning theory)Optimal controlEngineeringControl (management)MathematicsMathematical optimizationDistributed computing
DOInot available

Abstract

fetched live from OpenAlex

The paper presents an optimal fault tolerant control solution against the actuator faults in unmanned aerial vehicles (UAVs). A set of potential actuator fault scenarios are determined for the design of the fault tolerant controller. Controller design based on a linear quadratic (LQ) technique is chosen to be used for fault tolerant control design with the reformulated algebraic Riccati equation. In designing the fault tolerant controller gain, algebraic Riccati equation is reformulated to satisfy the stability and performance requirement under all the known fault scenarios. Weighting matrices Q and R are chosen in a way using method which can still maintain post-failure system's stability and acceptable performance with the desired redefined pole positions. The good performance is showed in simulations with respect to different fault scenarios considered in designing the fault tolerant controller for a 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)0.000
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: Empirical
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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