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Distributed Optimization With Event-triggered Communication via Input Feedforward Passivity

Li, M and Su, L and Liu, T Distributed Optimization With Event-triggered Communication via Input Feedforward Passivity. (Unpublished)

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Abstract

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

Item Type: Article
Uncontrolled Keywords: math.OC math.OC
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 15 Mar 2021 20:12
Last Modified: 19 Aug 2021 05:40
DOI: 10.1109/LCSYS.2020.3001998