自适应主被动网络多智能体系统
Adaptive Active-Passive Networked Multiagent Systems
摘要：
主动-被动多智能体系统由受输入影响的智能体（主动智能体）和没有输入的智能体（被动智能体）组成，其中主动和被动智能体角色被认为是可互换的，以捕获广泛的应用程序。主动-被动多智能体系统的控制中的一个挑战是存在的信息交换的不确定性，可以产生不期望的闭环系统性能。基于这一观点，本文提出了一种自适应控制算法来抑制这类多智能体系统的信息交换不确定性的负面影响。具体而言，通过估计这些不确定性，建议的自适应控制架构有能力恢复的主动-被动多智能体系统的性能在一个分布式的方式。因此，代理收敛到用户可调整的邻域的平均值的应用输入到活动代理。所提出的控制架构的功效也验证了从人-机器人协作的角度来看，其中一个人正在访问几个任务位置，和多智能体系统识别这些位置，并向他们移动的覆盖控制问题。
英文摘要：
Active-passive multiagent systems consist of agents subject to inputs (active agents) and agents with no inputs (passive agents), where active and passive agent roles are considered to be interchangeable in order to capture a wide array of applications. A challenge in the control of active-passive multiagent systems is the presence of information exchange uncertainties that can yield to undesirable closed-loop system performance. Motivated by this standpoint, this paper proposes an adaptive control algorithm for this class of multiagent systems to suppress the negative effects of information exchange uncertainties. Specifically, by estimating these uncertainties, the proposed adaptive control architecture has the ability to recover the active-passive multiagent system performance in a distributed manner. As a result, the agents converge to a user-adjustable neighborhood of the average of the applied inputs to the active agents. The efficacy of the proposed control architecture is also validated from a human-robot collaboration perspective, where a human is visiting several task locations, and the multiagent system identifies these locations and moves toward them as a coverage control problem.
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。
