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A predefined-time framework for average consensus and distributed optimization
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AI
DOI：10.1007/s11071-025-12008-z.png)
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Abstract
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.
Keywords:
Average consensus
Distributed optimization
Multi-agent systems
Fixed-time stability
Zero gradient sum
Journal
IF:
6
Papers:
1.4W
Citations:
4.7W
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