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Record W4367663078 · doi:10.1109/tcns.2023.3272291

Dual-Stage Heterogeneous Multiagent Systems Surrounding Control for a Motional Target

2023· article· en· W4367663078 on OpenAlexaff
Bowen Xu, Yaozhong Zheng, Yue Wu, Yang Shi

Bibliographic record

VenueIEEE Transactions on Control of Network Systems · 2023
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsUniversity of Victoria
FundersNational Natural Science Foundation of China
KeywordsDual (grammatical number)Computer scienceStage (stratigraphy)Multi-agent systemControl systemControl (management)Control theory (sociology)EngineeringArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

This article addresses the dynamic target surrounding control problem for a class of heterogeneous multiagent systems (MASs). To surround a motional target with a time-varying velocity, a distributed observer is established by employing perturbation system analysis, which can simultaneously retrieve both target state and dynamics. Then, a distributed dual-stage cooperative control scheme based on the output regulation principle is proposed to fulfill the even surrounding control mission, i.e., all the heterogeneous agents moving along a common circle around the dynamic target with an identical desired radius and evenly distributed phases. Afterward, an optimal selection protocol of the switching instant between the collective chasing and surrounding stages is provided as well. Significantly, sufficient conditions are derived to guarantee the asymptotical stability of such closed-loop heterogeneous MASs. Finally, numerical simulations are conducted to verify the effectiveness of the proposed surrounding control scheme.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.029
GPT teacher head0.255
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

Citations12
Published2023
Admission routes1
Has abstractyes

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