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Record W2031597959 · doi:10.1109/systol.2013.6693957

Actuator fault-tolerant control based on Gain-Scheduled PID with application to fixed-wing Unmanned Aerial Vehicle

2013· article· en· W2031597959 on OpenAlexaff
Iman Sadeghzadeh, Youmin Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFault Detection and Control Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsPID controllerFixed wingControl theory (sociology)RudderActuatorFlight envelopeQuadcopterFault (geology)Control engineeringEnvelope (radar)Fault toleranceAirplaneComputer scienceEngineeringAerodynamicsControl (management)WingAerospace engineeringArtificial intelligenceTemperature control

Abstract

fetched live from OpenAlex

This paper focuses on the application of widely used Proportional-Integral-Derivative (PID) technique to control an unmanned fixed-wing airplane for fault-free and faulty scenarios. The PID controller is used to control all six variables separately, which are Roll, Pitch, Yaw (Euler angles), X, Y and Z (Navigation). For fault cases, the Gain-Scheduled PID (GS-PID) control strategy is used to handle the hard-over fault which is injected to UAV's split rudder control surface for achieving fault-tolerant control. Different sets of pre-tuned PID gains are designed for different parts of flight envelope and fault cases.

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 categoriesInsufficient payload (model declined to judge)
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 score0.447
Threshold uncertainty score1.000

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)0.0000.001

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 head0.003
GPT teacher head0.186
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.

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

Citations17
Published2013
Admission routes1
Has abstractyes

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