Fully Distributed Consensus Tracking of Stochastic Nonlinear Multiagent Systems With Markovian Switching Topologies via Intermittent Control

The fully distributed consensus tracking of stochastic nonlinear multiagent systems (MASs) is investigated with Markovian switching topologies and intermittent control strategy, where the dynamics of agents are depicted by Ito differential equations and the leader's information is just known for a fraction of followers. The switching mechanism of interaction topologies is modeled as a Markov process. A novel class of fully distributed control protocols is proposed via intermittent control method, which is only associated with the relative state measurements of neighbors and does not involve any global information. Meanwhile, the control gains are designed to be intermittently adaptive, which can effectively reduce energy consumption and avoid the gains being larger than those needed in practice. Several sufficient conditions and corresponding proofs are provided by using the Lyapunov stability theory. Finally, numerical simulation is presented to state the feasibility of the theoretical results.