论文标题
连续时间和事件触发的线性多代理系统的在线优化
Continuous-Time and Event-Triggered Online Optimization for Linear Multi-Agent Systems
论文作者
论文摘要
本文研究了异构线性多机构系统的分散在线凸优化问题。代理可以访问其与自己的产出相关的时间变化的本地成本功能，并且它们之间也存在时间变化的耦合不平等约束。每个代理的目标是通过仅通过邻居之间的交流选择适当的本地操作来最大程度地减少全球成本函数。我们基于鞍点方法设计了一个分布式控制器，该方法可实现恒定的遗憾结合和sublrinear拟合。此外，为了减少沟通开销，我们提出了一种事件触发的通信方案，并表明在离散通信的情况下，仍然可以实现不断的遗憾和肌拟合绑定，而没有Zeno行为。提供了一个数值示例来验证所提出的算法。没有zeno行为。提供了一个数值示例来验证所提出的算法。
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.
