Event-triggered consensus of Markovian jumping multi-agent systems via stochastic sampling

This study investigates the issue of mean square consensus for multiple agents connected by a directed network. The graph of the network is supposed to have a spanning tree and each agent is taken as a Markovian jumping system. By utilising the event-triggered strategy, some sufficient conditions for consensus are presented whether the transition rates for the Markov chain being completely known or not. Furthermore, the event-triggered function designed in this study is dependent on the stochastic sampled-data from neighbouring agents. Theoretical results are provided according to the graph theory, Lyapunov functional and linear matrix inequality approach. Finally, a numerical example is given to demonstrate the effectiveness of the theoretical analysis.