Event-Based  State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises

Li Sheng; Zidong Wang; Lei Zou; Fuad E. Alsaadi

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Event-Based State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises

机译：时变随机噪声与状态和扰动有关的随机网络的基于事件的状态估计

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摘要

In this paper, the event-based finite-horizon H state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with stateand disturbance-dependent noises [also called (x, v)-dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

机译：本文针对一类具有状态和干扰相关噪声[也称为（x，v）相关噪声]的离散时变随机动力学网络，研究了基于事件的有限水平H状态估计问题。提出了一种事件触发方案，以减少传感器和估计器之间的数据传输频率，其中仅在满足某些条件时才发送信号。解决该问题的目的是设计一个时变状态估计器，以便通过可用的输出测量来估计网络状态。通过采用平方完成技术和随机分析方法，建立了充分的条件以确保状态估计的误差动态在有限的范围内满足指定的H性能约束。可以通过求解耦合后向递归Riccati差分方程来设计所需的估计器参数。最后，利用一个数值例子来证明所开发的状态估计方案的有效性。

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来源
《Neural Networks and Learning Systems, IEEE Transactions on》 |2017年第10期|2382-2394|共13页
作者
Li Sheng; Zidong Wang; Lei Zou; Fuad E. Alsaadi;
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作者单位

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China;

College of Electrical Engineering and Automation, Shandong University of Science and Technology, QingdaoChina;

College of Electrical Engineering and Automation, Shandong University of Science and Technology, QingdaoChina;

Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia;

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正文语种 eng
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Complex networks; State estimation; Stochastic processes; Synchronization; Sensors; Symmetric matrices; Difference equations;

机译：复杂网络状态估计随机过程同步传感器对称矩阵差分方程;

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Event-Based State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises

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