MétaCan
Menu
Back to cohort
Record W4383031784 · doi:10.11591/ijra.v12i3.pp228-239

Fault tolerance of a quadrotor via feedback linearization approach

2023· article· en· W4383031784 on OpenAlexaff
Ali Jebelli, Alireza Najafiyanfar, Arezoo Mahabadi, M.C.E. Yagoub

Bibliographic record

VenueIAES International Journal of Robotics and Automation (IJRA) · 2023
Typearticle
Languageen
FieldEngineering
TopicAdaptive Control of Nonlinear Systems
Canadian institutionsUniversity of OttawaUniversity of Alberta
Fundersnot available
KeywordsQuadcopterControl theory (sociology)Controller (irrigation)Feedback linearizationRotor (electric)PropellerPosition (finance)State spaceNonlinear systemAttitude controlLinearizationRotation (mathematics)Control systemGyroscopeEngineeringComputer scienceControl engineeringControl (management)MathematicsPhysicsAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A control algorithm is proposed to efficiently control the state, position, and height of a nonlinear dynamic model of a quadcopter. Based on feedback linearization, a state space model is presented for the system with the controller with a two-loop control structure designed and implemented in it. The inner and faster controller is responsible for adjusting the quadcopter height and angles, and the outer and slower controller is responsible for changing the desired figures of roll and pitch angles to control the system position. Whenever a rotor of the quadcopter rotor fails, the status and position of the system are converged and the system is stabilized. Simulation results based on different scenarios indicate the proper performance of the control system whenever there are external disturbances. Note that the gyroscopic effects because of<em> </em>the propeller rotation were not considered.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.394

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueIAES International Journal of Robotics and Automation (IJRA)Same topicAdaptive Control of Nonlinear SystemsFrench-language works237,207