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Record W2966520021

Decentralized Adaptive Fault-Tolerant Cooperative Control of Multi-UAVs Under Actuator Faults and Directed Communication Topology

2019· article· en· W2966520021 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

VenueAsian Control Conference · 2019
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsSynchronization (alternating current)ActuatorFault toleranceControl theory (sociology)Scheme (mathematics)Control engineeringComputer scienceDecentralised systemFault (geology)Adaptive controlControl (management)Attitude controlTopology (electrical circuits)EngineeringDistributed computingChannel (broadcasting)Artificial intelligenceComputer networkMathematics
DOInot available

Abstract

fetched live from OpenAlex

In this paper, a decentralized adaptive fault-tolerant cooperative control scheme is proposed to achieve the attitude synchronization tracking control of multiple unmanned aerial vehicles (multi-UAVs) with actuator faults and directed communication topology. To alleviate the adverse effects caused by the in-flight actuator faults, adaptive laws are constructed and integrated into the developed attitude synchronization control scheme to enhance the formation flight safety by using the neural networks. The distinctive feature of the proposed method is to address the fault-tolerant attitude synchronization tracking control problem in a decentralized framework with directed communications. It is shown that the tracking and synchronization of multi-UAVs with respect to the desired attitudes can be achieved. Simulation results are provided to demonstrate the effectiveness of the proposed control approach.

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: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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