.png){kind=icon}
返回
Distributed data-driven observer for linear time invariant systems
.png){kind=icon}
PRE
AI
DOI：10.1002/acs.3100.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper is concerned with distributed data-driven observer design problem. The existing data-driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal. On the other hand, traditional model-based distributed state estimation methods generally assume that the processes are decomposed according to the known process models, while in data-driven approaches there is no such information available. The main goal of this paper is to extend the centralized data-driven observer design approach to the distributed framework. The stability of the proposed data-driven distributed observer is also proved analytically. A quadruple-tank process is simulated to demonstrate the performance of the proposed scheme.
Keyword:
data-driven observer
distributed observer
quadruple-tank process
state estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
2.6K
被引数:
3.6K
机构
引用论文
oskar organizes the germ plasm and directs localization of the posterior determinant nanos.png)
Cell
CellIF0
PREAI
分享收藏The effect of home education on myopia progression in children during the COVID-19 pandemicEye
OAAI
EyeIF0
OAAI分享收藏
分享收藏分享收藏A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks传感器网络上一般非线性系统的事件触发滤波的集合成员方法
PREAI
分享收藏
查看更多内容