This paper studies the decentralized online convex optimization problem for heterogeneous linear multi-agent systems. Agents have access to their time-varying local cost functions related to their own outputs, and there are also time-varying coupling inequality constraints among them. The goal of each agent is to minimize the global cost function by selecting appropriate local actions only through communication between neighbors. We design a distributed controller based on the saddle-point method which achieves constant regret bound and sublinear fit bound. In addition, to reduce the communication overhead, we propose an event-triggered communication scheme and show that the constant regret bound and sublinear fit bound are still achieved in the case of discrete communications with no Zeno behavior. A numerical example is provided to verify the proposed algorithms.with no Zeno behavior. A numerical example is provided to verify the proposed algorithms.
@article{arxiv.2207.00999,
title = {Continuous-Time and Event-Triggered Online Optimization for Linear Multi-Agent Systems},
author = {Yang Yu and Xiuxian Li and Li Li and Lihua Xie},
journal= {arXiv preprint arXiv:2207.00999},
year = {2022}
}