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Finite-time distributed H-infinity filtering for Takagi-Sugeno fuzzy system with uncertain probability sensor saturation under switching network topology: Non-PDC approach

机译:有限时间分布式H-Infinity滤波,用于开关网拓扑下不确定概率传感器饱和度的Takagi-Sugeno模糊系统:非PDC方法

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This paper mainly investigates the stochastic finite-time distributed H-infinity filtering problem for more general Takagi-Sugeno fuzzy systems (TSFSs) with immeasurable premise variables over wireless sensor networks (WSNs) with switching topology. The practical factors including sensor saturation and measurement missing, which are modeled by mutually independent Bernoulli processes with uncertain probability, are taken into account. Utilizing non-parallel-distributed-compensation (non-PDC) scheme, a switching-type distributed filter based on estimated premise variables is designed to realize the sharing of filtering information and measurement information. A distributed robust filtering method is proposed to analyze the distributed filtering error system (DFES) containing unknown premise variables. Then by constructing a model-dependent fuzzy Lyapunov function, new less conservative sufficient conditions in term of LMIs are obtained to ensure the DFES stochastic finite-time bounded and achieving a modified H-infinity performance index under bounded disturbance. By solving a convex optimization problem, the filter gain parameters and average dwell time of topology switching signal are determined. Finally, a tunnel diode circuit in sensor networks is considered to verify the theoretical findings. (C) 2019 Elsevier Inc. All rights reserved.
机译:本文主要调查随着无线传感器网络(WSN)的不可估量的前提变量,对更全面的Takagi-Sugeno模糊系统(TSFS)进行了无线传感器网络(WSNS)的随机有限时间分布式H-Infinity过滤问题。考虑到包括传感器饱和度和测量值的实际因素,这些因素是由具有不确定概率的相互独立的Bernoulli进程建模的。利用非平行分布式补偿(非PDC)方案,基于估计的前提变量的开关类型分布式滤波器旨在实现过滤信息和测量信息的共享。提出了一种分布式鲁棒滤波方法,用于分析包含未知前提变量的分布式滤波错误系统(DFE)。然后通过构建模型依赖模糊Lyapunov函数,获得LMI期间的新的保守较低的条件,以确保DFES随机有限时间限定并在有界干扰下实现改进的H-Infinity性能指标。通过求解凸优化问题,确定滤波器增益参数和拓扑开关信号的平均停留时间。最后,认为传感器网络中的隧道二极管电路被认为是验证理论发现。 (c)2019 Elsevier Inc.保留所有权利。

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