IFP-based Distributed Optimization With Event-triggered Communication.

In this work, we address the distributed optimization problem with event-triggered communication by introducing the notion of input feedforward passivity (IFP). First, we analyze a distributed continuous-time algorithm over uniformly jointly strongly connected balanced digraphs and show its exponential convergence over strongly connected digraphs. Then, we propose an event-triggered communication mechanism for this algorithm. Next, we discretize the continuous-time algorithm by the forward Euler method and show that the discretization can be seen as a stepsize dependent passivity degradation of the input feedforward passivity. The discretized system preserves IFP property and enables the same event-triggered communication mechanism but without Zeno behavior due to its sampling nature. Finally, a numerical example is presented to illustrate our results.