论文标题
分布式优化通过输入前馈电而通过事件触发的通信进行优化
Distributed Optimization With Event-triggered Communication via Input Feedforward Passivity
论文作者
论文摘要
在这项工作中，我们通过输入前馈动性（IFP）的概念解决了分布式优化问题，并通过事件触发的通信解决了分布式优化问题。首先，我们在基于IFP的框架中分析了分布式的连续时间算法在均匀连接的平衡挖掘机上。然后，我们为该算法提出了一个分布式事件触发的通信机制。接下来，我们通过向前的Euler方法离散连续的时间算法，其恒定步骤与网络大小无关，并证明可以将离散化视为输入feedforward villedivity的步骤依赖性的被动式降解。因此，离散的系统保留了IFP属性并实现相同的事件触发的通信机制，但由于离散时间性质而没有ZENO行为。最后，提出了一个数字示例来说明我们的结果。
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.
