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Scaled consensus of descriptor multi-agent systems

机译:描述符多主体系统的尺度共识

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Multi-agent systems have been studied from different perspectives. In this paper, the problem of scaled consensus is considered for descriptor multi-agent systems. In fact, each agent is considered as a descriptor system. This is significant due to the fact that descriptor systems describe a larger group of systems. Furthermore, an algorithm is proposed to drive the agents to different consensus points, which is denoted as scaled consensus. This can also have very important applications in areas such as multi-robot systems where each robot needs to perform a different task. Moreover, in contrast with the works in the literature on scaled consensus, higher order systems are allowed in this paper. Also, as practical systems may suffer from time delay, these systems under a constant delay are considered. Finally, numerical examples are provided to further demonstrate the effectiveness of the proposed algorithms.
机译:已经从不同角度研究了多智能体系统。在本文中,考虑了描述符多智能体系统的尺度一致问题。实际上,每个代理都被视为描述符系统。由于描述符系统描述了一组较大的系统,因此这一点很重要。此外,提出了一种算法来驱动代理到不同的共识点,这被称为缩放共识。这在诸如多机器人系统等每个机器人都需要执行不同任务的领域中也可能具有非常重要的应用。此外,与文献中关于规模共识的工作相反,本文允许使用更高阶的系统。另外,由于实际系统可能会受到时间延迟的影响,因此考虑了在恒定延迟下的这些系统。最后,提供了数值示例,以进一步证明所提出算法的有效性。

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