Variance‐constrained state estimation for networked multi‐rate systems with measurement quantization and probabilistic sensor failures
Variance‐constrained state estimation for networked multi‐rate systems with measurement quantization and probabilistic sensor failures
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DOI：
10.1002/rnc.3520
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发表时间：
2016-11
影响因子：
3.9
通讯作者：
Yong Zhang;Zidong Wang;Lifeng Ma
中科院分区：
文献类型：
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作者：
Yong Zhang;Zidong Wang;Lifeng Ma
本文研究了一类具有网络诱导概率传感器失效的网络化多速率系统的方差约束状态估计问题和测量量化问题。传感器故障的随机特性在区间[0,1]上由相互独立的随机变量控制。应用提升技术，建立了一个增强系统模型，便于对底层NMSs进行状态估计。利用随机分析方法，得到了增广系统指数均方稳定性得到保证、H∞性能约束得到满足、稳态估计误差个体方差约束得到满足的充分条件。在此基础上，将求解的方差约束状态估计问题转化为一个半确定规划方法求解的凸优化问题。此外，利用某些矩阵不等式的可行性，得到了期望估计量增益的显式表达式。考虑了关于H∞性能指标和加权误差方差的两个附加优化问题。最后，通过仿真实例验证了所提状态估计方法的有效性。版权所有©2016 John Wiley & Sons， Ltd。
This paper is concerned with the variance‐constrained state estimation problem for a class of networked multi‐rate systems (NMSs) with network‐induced probabilistic sensor failures and measurement quantization. The stochastic characteristics of the sensor failures are governed by mutually independent random variables over the interval [0,1]. By applying the lifting technique, an augmented system model is established to facilitate the state estimation of the underlying NMSs. With the aid of the stochastic analysis approach, sufficient conditions are derived under which the exponential mean‐square stability of the augmented system is guaranteed, the prescribed H∞ performance constraint is achieved, and the individual variance constraint on the steady‐state estimation error is satisfied. Based on the derived conditions, the addressed variance‐constrained state estimation problem of NMSs is recast as a convex optimization one that can be solved via the semi‐definite program method. Furthermore, the explicit expression of the desired estimator gains is obtained by means of the feasibility of certain matrix inequalities. Two additional optimization problems are considered with respect to the H∞ performance index and the weighted error variances. Finally, a simulation example is utilized to illustrate the effectiveness of the proposed state estimation method. Copyright © 2016 John Wiley & Sons, Ltd.