An Event-Triggered Approach to State Estimation for a Class of Complex Networks With Mixed Time Delays and Nonlinearities
An Event-Triggered Approach to State Estimation for a Class of Complex Networks With Mixed Time Delays and Nonlinearities
复制标题
一类具有混合时滞和非线性的复杂网络的事件触发状态估计方法
DOI：
10.1109/tcyb.2015.2478860
复制
发表时间：
2016-11
影响因子：
11.8
通讯作者：
Guoliang Wei
中科院分区：
文献类型：
--
作者：
Licheng Wang;Zidong Wang;Tingwen Huang;Guoliang Wei
研究了一类具有非线性、混合时滞和随机噪声的离散复杂网络的状态估计问题。构造了一组基于事件的状态估计器，以减少通信信道中不必要的数据传输。与传统的状态估计器的测量信号下接收的周期性时钟驱动的规则相比，基于事件的估计器只更新的测量信息时，预先指定的“事件”被违反的传感器。研究离散时间复杂网络的事件估计器的分析和设计问题，使得估计误差在均方上是指数有界的。采用随机分析方法和李雅普诺夫理论相结合，得到了确保期望估计量存在的充分条件，并推导了估计误差的上界。通过使用凸优化技术，所需的估计器的增益参数提供了一个明确的形式。最后，通过仿真实例验证了该估计策略的有效性.
In this paper, the state estimation problem is investigated for a class of discrete-time complex networks subject to nonlinearities, mixed delays, and stochastic noises. A set of event-based state estimators is constructed so as to reduce unnecessary data transmissions in the communication channel. Compared with the traditional state estimator whose measurement signal is received under a periodic clock-driven rule, the event-based estimator only updates the measurement information from the sensors when the prespecified “event” is violated. Attention is focused on the analysis and design problem of the event-based estimators for the addressed discrete-time complex networks such that the estimation error is exponentially bounded in mean square. A combination of the stochastic analysis approach and Lyapunov theory is employed to obtain sufficient conditions for ensuring the existence of the desired estimators and the upper bound of the estimation error is also derived. By using the convex optimization technique, the gain parameters of the desired estimators are provided in an explicit form. Finally, a simulation example is used to demonstrate the effectiveness of the proposed estimation strategy.
登录
查看更多内容
影响因子：
6.8
作者：
Donkers, M. C. F.;Heemels, W. P. M. H.
通讯作者：
Heemels, W. P. M. H.
具有通信延迟的离散时间多智能体共识的分布式事件触发方案
影响因子：
2.6
作者：
Li Lulu;Ho Daniel W. C.;Xu Shengyuan
通讯作者：
Xu Shengyuan
影响因子：
6.4
作者：
Hu, Jun;Wang, Zidong;Stergioulas, Lampros K.
通讯作者：
Stergioulas, Lampros K.
影响因子：
6.8
作者：
Chen Peng;Q. Han
通讯作者：
Chen Peng;Q. Han
DOI：
10.1016/j.ins.2014.08.047
发表时间：
2015-01
期刊：
Inf. Sci.
影响因子：
--
作者：
Xiaohua Ge;Qing-Long Han
通讯作者：
Xiaohua Ge;Qing-Long Han