A Continuous-Time Algorithm for Distributed Optimization With Nonuniform Time-Delay Under Switching and Unbalanced Digraphs

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-10-23 DOI:10.1109/TSIPN.2024.3485549

Wenbo Zhu;Wenqiang Wu;Qingling Wang

引用次数: 0

Abstract

This paper studies the distributed optimization of continuous-time multi-agent systems with time-delay under switching digraphs. An auxiliary system which only requires the information of the number of adjacent agents is first constructed, then a class of new distributed optimization algorithms are proposed. As an application, we extend above algorithms to address distributed economic dispatch issues for smart grids. It is theoretically shown that the new illustrated distributed control strategies can asymptotically realize optimal consensus for multi-agent systems and optimal economic dispatch for smart grids, where the communication time-delay can be nonuniform, and the switching digraphs are uniformly jointly strongly connected. Finally, two simulation examples are provided to validate theoretical results.

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切换和不平衡图谱下非均匀时延分布式优化的连续时间算法

本文研究了切换数字图下具有时延的连续时间多代理系统的分布式优化问题。首先构建了一个只需要相邻代理数量信息的辅助系统，然后提出了一类新的分布式优化算法。作为应用，我们将上述算法扩展到解决智能电网的分布式经济调度问题。理论证明，新的分布式控制策略可以渐近地实现多代理系统的最优共识和智能电网的最优经济调度，其中通信时延可以是非均匀的，而开关数字图是均匀联合强连接的。最后，还提供了两个仿真实例来验证理论结果。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.