A Cooperative Fault Detection Approach for Stochastic Multi-Agent Systems Using Maximum Likelihood Estimation Method

This study addresses the fault detection problem in multi-agent systems (MASs) with additive faults and stochastic uncertainties. The main focus is on enhancing the fault detection capability of each agent through a cooperative fault detection scheme, fostering cooperation between agents in two scen...

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Vydáno v:Journal of systems science and complexity Ročník 37; číslo 6; s. 2451 - 2465
Hlavní autoři: Yang, Chen, Li, Yan, Chen, Qijun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
Témata:
Algorithms
Complex Systems
Control
Cooperation
Euclidean space
Fault detection
Kalman filters
Mathematics
Mathematics and Statistics
Mathematics of Computing
Maximum likelihood estimation
Multiagent systems
Operations Research/Decision Theory
Probabilistic models
Statistics
Systems Theory
Uncertainty
maximum likelihood estimation
multi-agent systems
Cooperative fault detection
ISSN:1009-6124, 1559-7067
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Shrnutí:This study addresses the fault detection problem in multi-agent systems (MASs) with additive faults and stochastic uncertainties. The main focus is on enhancing the fault detection capability of each agent through a cooperative fault detection scheme, fostering cooperation between agents in two scenarios. For Gaussian uncertainties, one scheme is developed using the maximum likelihood estimation (MLE) matching expectation maximization (EM) algorithm. Additionally, a novel cooperative fault detection scheme is introduced to handle non-Gaussian uncertainties, where the cooperation mechanism among agents is determined by approximating non-Gaussian uncertainties using the Gaussian mixture model (GMM). The effectiveness and improvements of the proposed cooperative fault detection method are validated through numerical simulations.
Bibliografie:ObjectType-Article-1
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ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-024-3293-y