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Record W2571988845 · doi:10.1002/rnc.3734

Robust output synchronization of linear multi‐agent systems with constant disturbances via integral control

2017· article· en· W2571988845 on OpenAlexaff
Qingling Wang, Xianghui Cao, Changyin Sun

Bibliographic record

VenueInternational Journal of Robust and Nonlinear Control · 2017
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsControl theory (sociology)Synchronization (alternating current)Computer scienceConstant (computer programming)Controller (irrigation)Protocol (science)Observer (physics)State (computer science)Control (management)Multi-agent systemAlgorithmArtificial intelligenceComputer network

Abstract

fetched live from OpenAlex

Summary This paper solves the robust output synchronization problem for a group of linear agents subject to constant disturbances on directed graphs. A new distributed control protocol with integral action is first proposed with the assumption that each agent has access to its own state. Then, an observer‐based implementation of the control protocol is illustrated for the case that the agents do not have access to their states. Compared with existing methods, the new distributed protocol does not require exchanging of the controller states or output states among neighboring agents. Finally, simulation results of a group of low‐speed experimental unmanned aerial vehicles show the effectiveness of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.251
Teacher spread0.226 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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