Dynamic Weighted Average Consensus of Open Time-Varying Multi-Agent Systems on Time Scales via Sampled-Data Impulsive Communication

The fundamental purpose of this study was to investigate the dynamic weighted average consensus of time-varying open multi-agent systems (MASs) on time scales, where the open feature refers to the random arrivals and departures of nodes at each opening moment. Aiming at a class of time-varying open MASs on time scales, we set each opening instant as the sampling instant in advance and proposed a control method combining data sampling and impulses. First, the nodes of open MASs are divided into three categories, and the concept of an open state vector is introduced to solve the problem of inconsistent state vectors before and after each opening instant. Then, based on the time scale theory and Lyapunov stability theory, a sufficient condition for dynamic weighted average bounded consensus is obtained. In addition, when the system involves immeasurable information, an impulsive observer is introduced to reconstruct the information of the original system, and the impulsive adjustment item is constructed using the output vector of the two systems to ensure that the original system can still achieve the dynamic weighted average consensus. Finally, two simulation examples were used to verify the validity of the theoretical analysis.