A Distributed Optimization Approach for Multi-Agent Systems over Delaying Networks

Farshad Rahiimi
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Abstract

—This paper investigates a novel method to solve distributed optimization problems in the presence of communication delays between the networked agents that cooperate together to find an optimal solution of a global cost function composed of local ones. In the problem of distributed optimization in a network of multi-agent because of existing phenomena such as communication delay, deriving approaches having appropriate performance so that the states of all agents converge to the same value always has been a substantial challenge. Delay-dependent conditions in the form of linear matrix inequities are derived to analyze the convergence of the introduced scheme to the optimal solution. It is demonstrated that the maximum allowable time delay in the network and convergence rate of the optimization procedure are increased by the suggested strategy. Finally, comparative simulation results are considered to illustrate the superior performance of the introduced scheme compared to a rival one in the literature.
延迟网络上多智能体系统的分布式优化方法
本文研究了一种解决存在通信延迟的分布式优化问题的新方法,这些网络代理共同合作寻找由局部成本函数组成的全局成本函数的最优解。在多智能体网络的分布式优化问题中,由于存在通信延迟等现象,如何找到性能合适的方法使所有智能体的状态收敛到同一值一直是一个很大的挑战。导出了线性矩阵不等式形式的时滞相关条件,分析了所引入的方案对最优解的收敛性。结果表明,该策略提高了网络的最大允许时延和优化过程的收敛速度。最后,对比仿真结果表明,与文献中的竞争方案相比,所引入的方案具有优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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