TY - JOUR
AU - Chen, Qijun
AB - 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.
TI - A Cooperative Fault Detection Approach for Stochastic Multi-Agent Systems Using Maximum Likelihood Estimation Method
JF - Journal of Systems Science and Complexity
DO - 10.1007/s11424-024-3293-y
DA - 2024-12-01
UR - https://www.deepdyve.com/lp/springer-journals/a-cooperative-fault-detection-approach-for-stochastic-multi-agent-y1Wehrklyt
SP - 2451
EP - 2465
VL - 37
IS - 6
DP - DeepDyve
ER -