Optimal Mass Transport and Kernel Density Estimation for State-Dependent Networked Dynamic Systems
Optimal Mass Transport and Kernel Density Estimation for State-Dependent Networked Dynamic Systems
复制标题
状态相关网络动态系统的最优质量传递和核密度估计
DOI：
10.1109/cdc.2018.8619808
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
M. Mesbahi
中科院分区：
文献类型：
--
作者：
Mathias Hudoba de Badyn;Utku Eren;Behçet Açikmese;M. Mesbahi
状态相关的网络动态系统是代理之间的互连随着代理状态的函数而变化的系统。此类系统是高度非线性的，文献中缺乏对其控制的内聚策略。在本文中，我们提出了两种与此类系统的密度控制相关的技术。智能体状态最初根据某种密度分布，并且设计反馈定律以将智能体移动到目标密度分布。我们使用最佳质量传输来设计前馈控制律，推动代理达到目标密度。然后，使用由状态相关动力学施加的约束的核密度估计来允许每个智能体估计智能体的局部密度。
State-dependent networked dynamic systems are ones where the interconnections between agents change as a function of the states of the agents. Such systems are highly nonlinear, and a cohesive strategy for their control is lacking in the literature. In this paper, we present two techniques pertaining to the density control of such systems. Agent states are initially distributed according to some density, and a feedback law is designed to move the agents to a target density profile. We use optimal mass transport to design a feedforward control law propelling the agents towards this target density. Kernel density estimation, with constraints imposed by the state-dependent dynamics, is then used to allow each agent to estimate the local density of the agents.
影响因子：
6.8
作者：
Chen, Yongxin;Georgiou, Tryphon T.;Pavon, Michele
通讯作者：
Pavon, Michele