Consensus of Stochastic Multi-agent Systems via Observer-based Event-triggered Control Strategy

Due to the fact that the accurate state of object in real life may not be available in real-time, it is necessary to use observations for optimal state estimation. The consensus of Multi-Agent Systems (MASs) with stochastic process noise and measurement noise is investigated based on observations. Firstly, according to the theory of Kalman filtering, the optimal state estimation of each agent can be obtained and the state information is sent to adjacent agents through the network communication topology. Then, within the same time interval, a new event triggered measurement error function is defined for adjacent nodes, which can obtain the non-triggered state of adjacent nodes at that triggering moment. Additionally, a corresponding event-triggered controller is matched, effectively reducing the controller’s update frequency, and the criterion for the mean square consensus theorem of the MASs is obtained. Finally, the effectiveness of the proposed event triggered control protocol is verified through numerical simulation.