This paper studies a consensus problem for a kind of stochastic multi-agent systems (SMAS). First, a reduced-order observer is designed to estimate unknown states in SMASs. Second, an event-triggered adaptive output feedback control method is presented. It can reduce the controller updates and communication burden. Moreover, the radial basis function neural networks are applied to approximate the unknown functions in systems. Finally, it is demonstrated that the proposed control scheme can achieve finite-time practical consensus for SMASs. Simulation results are provided to illustrate the effectiveness of the theoretical analysis.
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