.png){kind=icon}
返回
A predefined-time framework for average consensus and distributed optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s11071-025-12008-z.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a generalized framework for average consensus and distributed optimization in first-order multi-agent systems under dynamic undirected networks. The framework introduces a family of predefined-time consensus functions that are not based on the homogeneity principle, in which the convergence-time bound is a user-defined parameter, regardless of the initial condition. Moreover, unlike many piecewise algorithms in the current literature, the proposed distributed optimization protocol is based on the Zero-Gradient-Sum approach but does not require local minimization. Extensive numerical simulations are conducted to illustrate the efficacy of this framework.
Keyword:
Average consensus
Distributed optimization
Multi-agent systems
Fixed-time stability
Zero gradient sum
期刊
IF:
6
论文数:
1.4W
被引数:
4.6W
机构
引用论文
.png){kind=icon}
Distributed convex optimization based on zero‐gradient‐sum algorithm under switching topology基于零梯度和算法的切换拓扑分布式凸优化
OAAI
OAAI分享收藏
查看更多内容