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Record W2912391416 · doi:10.1109/tmech.2019.2895222

Attitude Synchronization For Multiple 3-DOF Helicopters With Actuator Faults

2019· article· en· W2912391416 on OpenAlexaff
Huiliao Yang, Bin Jiang, Hugh H. T. Liu, Hao Yang, Qingrui Zhang

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2019
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsUniversity of Toronto
FundersFundamental Research Funds for the Central UniversitiesGraduate Research and Innovation Projects of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsActuatorControl theory (sociology)Synchronization (alternating current)Attitude controlComputer scienceFault toleranceFault (geology)Control (management)Control engineeringTrack (disk drive)EngineeringArtificial intelligenceDistributed computingChannel (broadcasting)

Abstract

fetched live from OpenAlex

The attitude synchronization for multiple three-degree-of-freedom helicopters in the presence of system uncertainties and time-varying actuator faults is investigated. A fault-tolerant cooperative control algorithm based on robust adaptive control is proposed to synchronize attitudes of multiple helicopter systems in the case where some of the helicopters are subjected to both partial losses of control effectiveness and additive actuator faults. The proposed design is a distributed control method that allows each vehicle to track external reference signals merely in light of its neighborhood information. With the proposed design, time-varying actuator faults are tolerated without sacrificing the attitude synchronization performance. The effectiveness of the proposed control method is verified via a comparative experimental study.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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)0.0000.000

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 head0.009
GPT teacher head0.215
Teacher spread0.207 · 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

Citations40
Published2019
Admission routes1
Has abstractyes

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