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arXiv 2204.04415eess.SYcs.SY
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面向具有时变参考信号的智能体网络的鲁棒动态平均一致性

Robust Dynamic Average Consensus for a Network of Agents with Time-varying Reference Signals

Solomon Gudeta, Ali Karimoddini, Mohammadreza Davoodi

AI总结:

针对智能体网络中时变参考信号的平均估计问题,提出基于边、采用平滑函数的连续DAC算法,可在智能体动态进出时避免抖振并保证零稳态误差。

摘要:

本文提出了连续动态平均一致性(DAC)算法,供一组智能体协同估计其时变参考信号的平均值。我们提出的一致性算法对智能体加入和离开网络具有鲁棒性,同时避免抖振现象,并保证零稳态一致性误差。我们的算法是基于边的协议,在其内部结构中采用平滑函数以避免抖振效应。此外,每个智能体仅能执行局部计算,并且只能与其局部邻居通信。对于平衡且强连通的底层通信图,我们给出了收敛性分析,以确定一致性设计参数,这些参数保证智能体对其平均值的估计渐近收敛到智能体时变参考信号的平均值。我们提供了仿真结果,以验证所提出的一致性算法,并将所提算法与文献中的现有算法进行性能比较。

英文摘要:

This paper presents continuous dynamic average consensus (DAC) algorithms for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose consensus algorithms that are robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithms are edge-based protocols with smooth functions in their internal structure to avoid the chattering effect. Furthermore, each agent is only capable of performing local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the agents' estimate of their average to asymptotically converge to the average of the time-varying reference signals of the agents. We provide simulation results to validate the proposed consensus algorithms and to perform a performance comparison of the proposed algorithms to existing algorithms in the literature.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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