LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Event-triggered consensus control for stochastic multi-agent systems under state-dependent topology

Photo from wikipedia

In this study, the mean square consensus problem for stochastic multi-agent systems with nonlinear protocols is concerned. In particular, the topological structure of the system is characterised by state-dependent directed… Click to show full abstract

In this study, the mean square consensus problem for stochastic multi-agent systems with nonlinear protocols is concerned. In particular, the topological structure of the system is characterised by state-dependent directed graph, in which the edges are described by nonlinear function related to the states and possess much better performance than before. A novel proposition based on matrix theory is proposed to conquer the challenges of time-invariant asymmetric matrix. In view of the communication burden and practically, a time-dependent event-triggered strategies are explored, where the control input on each agent is updated only when a certain triggering condition is violated. Then, some sufficient conditions for mean square consensus of stochastic multi-agent with state-dependent topology are established. Furthermore, a positive lower bound for inter-event times can be found such that the Zeno phenomenon is eliminated. Finally, a simulation example is utilised to illustrate the validly of developed theoretical results and the effectiveness of the control protocol.

Keywords: agent; control; state dependent; multi agent; topology; stochastic multi

Journal Title: International Journal of Control
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.