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Bipartite Consensus for Multi-Agent Systems With Noises Over Markovian Switching Topologies
Neurocomputing ( IF 6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.neucom.2020.08.005 Yingxue Du , Yijing Wang , Zhiqiang Zuo
Neurocomputing ( IF 6 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.neucom.2020.08.005 Yingxue Du , Yijing Wang , Zhiqiang Zuo
Abstract In this paper, we investigate the distributed control problem for multi-agent systems (MASs) subject to multiplicative and additive noises over switching networks, where both cooperative and antagonistic interactions coexist. The communication topology is governed by a continuous-time Markovian chain. A stochastic approximation technique is utilized to handle stochastic bipartite consensus with communication noises. The major challenge, due to the fact that the intensity of the multiplicative noise is nonlinearly coupled with the distance between agents, is that the coexistence of antagonistic information and multiplicative noise makes the multiplicative noise term impossible to be converted into an error form. This leads to the inapplicability of the Lyapunov-based method. To cope with this, we first show the boundedness of agents’ states where the second moment approach is employed. Based on it, the mean square and almost surely bipartite consensus are achieved under some mild requirements. The efficiency of the proposed method is supported by an example.
中文翻译:
马尔可夫交换拓扑结构噪声多代理系统的二部共识
摘要 在本文中,我们研究了多智能体系统 (MAS) 的分布式控制问题,该系统受开关网络上的乘法和加法噪声影响,其中合作和对抗交互共存。通信拓扑由连续时间马尔可夫链控制。随机近似技术被用来处理带有通信噪声的随机二分共识。由于乘法噪声的强度与代理之间的距离非线性耦合,主要挑战在于对抗信息和乘法噪声的共存使得乘法噪声项无法转换为误差形式。这导致基于李雅普诺夫的方法不适用。为了应对这种情况,我们首先展示了使用二阶矩方法的代理状态的有界性。基于它,在一些温和的要求下实现了均方和几乎可以肯定的两方共识。所提出方法的效率得到了一个例子的支持。
更新日期:2021-01-01
中文翻译:
马尔可夫交换拓扑结构噪声多代理系统的二部共识
摘要 在本文中,我们研究了多智能体系统 (MAS) 的分布式控制问题,该系统受开关网络上的乘法和加法噪声影响,其中合作和对抗交互共存。通信拓扑由连续时间马尔可夫链控制。随机近似技术被用来处理带有通信噪声的随机二分共识。由于乘法噪声的强度与代理之间的距离非线性耦合,主要挑战在于对抗信息和乘法噪声的共存使得乘法噪声项无法转换为误差形式。这导致基于李雅普诺夫的方法不适用。为了应对这种情况,我们首先展示了使用二阶矩方法的代理状态的有界性。基于它,在一些温和的要求下实现了均方和几乎可以肯定的两方共识。所提出方法的效率得到了一个例子的支持。