Robust Consensus Problem of Data-sampled Networked Multi-agent Systems with Delay and Noise

This paper proposes an observer-based control strategy for networked multi-agent systems with timevarying communication delays and random white noises under both fixed topology and Bernoulli switching topology. First, a queuing mechanism is introduced and thus a team of agents can be modeled as a system with constant delay. Then, using the system transformation method, the robust mean-square consensus problem of multi-agent systems can be converted into the robust mean-square stability problem of an equivalent system, and some equivalent conditions concerning the robust mean-square consensus of networked multi-agent systems are presented, whose related observer-based stabilizability criteria can be established in the form of linear matrix inequalities (LMIs). Furthermore, if the LMIs are feasible, the multi-agent systems achieve robust mean-square consensus if and only if the directed graph has a directed spanning tree (fixed topology) or the union of graphs has a directed spanning tree (Bernoulli switching topology). Finally, numerical simulations are given to illustrate the effectiveness of the obtained theoretical results. Key–Words: Networked multi-agent systems, robust mean-square consensus, delay, sampled control, general linear dynamics. inequalities(LMIs).