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Record W1560616049 · doi:10.1108/20496421311298125

A robust semi‐decentralized fault detection strategy for multi‐agent systems

2013· article· en· W1560616049 on OpenAlexaff
Mohammadreza Davoodi, K. Khorasani, Heidar Ali Talebi, Hamidreza Momeni

Bibliographic record

VenueInternational Journal of Intelligent Unmanned Systems · 2013
Typearticle
Languageen
FieldComputer Science
TopicDistributed Control Multi-Agent Systems
Canadian institutionsConcordia University
Fundersnot available
KeywordsFault detection and isolationComputer scienceMulti-agent systemFault (geology)Distributed computingMathematical optimizationLinear systemControl theory (sociology)MathematicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

Purpose The aim of this paper is to address the problem of fault detection (FD) of linear continuous‐time multi‐agent systems. Design/methodology/approach A mixedH∞/H−formulation of the FD problem using semi‐decentralized filters is presented. Findings It is shown that through a decomposition approach the drawbacks of the existing distributed FD design methods in multi‐agent systems can be effectively tackled. An extended linear matrix inequality (LMI) characterization is used to reduce the conservativeness of the design solution by introducing additional matrices in order to eliminate the couplings of the Lyapunov matrices with the agent's matrices. Research limitations/implications It is shown that by applying the proposed decomposition approach the FD problem of multi‐agent systems can be solved by analyzing the problem of a set of decoupled systems whose order and complexity are equal to that of a single agent. This procedure will be useful for both simplifying the computational cost of the solution as well as for developing a fault detection filter having a semi‐decentralized architecture. Practical implications Application of this methodology to a network of micro‐air vehicles (MAVs) illustrates the effectiveness and capabilities of the proposed design methodology. Social implications The feasibility of the use of reliable and self‐healing network of unmanned systems, cooperative networks, and multi‐agent systems will be significantly enhanced and improved by the development of advanced fault detection and isolation (FDI) technologies. Originality/value A semi‐decentralized fault detection (FD) methodology is developed for linear multi‐agent networked systems to reduce the order and complexity of the observers at each agent. A mixedH∞/H−formulation of the FD problem by using semi‐decentralized filters is presented. Using this approach each agent can not only detect itsownfaults but also is able to detect itsnearest neighboragents’ faults.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.302
Teacher spread0.227 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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Same venueInternational Journal of Intelligent Unmanned SystemsSame topicDistributed Control Multi-Agent SystemsFrench-language works237,207