Distributed event-triggered scheme for a convex optimization problem in multi-agent systems

In this paper, a distributed event-triggered algorithm was proposed to solve a convex quadratic optimization problem of multi-agent systems under undirected and connected topologies. The event-triggered condition of each agent just requires its own state value and the state values of its neighbors at the triggering time, and hence the continuous communication and calculation are not required. Moreover, the minimum event-triggered interval is bounded by the sampling time and the Zeno behavior is therefore naturally avoided. The result is also extended to the networks with undirected and switching topologies. Numerical simulations show the effectiveness of the proposed approach.