A recursive approach to non-fragile filtering for networked systems with stochastic uncertainties and incomplete measurements
A recursive approach to non-fragile filtering for networked systems with stochastic uncertainties and incomplete measurements
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DOI：
10.1016/j.jfranklin.2015.02.002
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
Jun Hu;Jinling Liang;Dongyan Chen;D. Ji;Junhua Du
中科院分区：
文献类型：
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作者：
Jun Hu;Jinling Liang;Dongyan Chen;D. Ji;Junhua Du
本文研究了一类具有随机不确定性和不完全测量的网络时变非线性系统的非脆弱递归滤波问题。通过采用随机克罗内克德尔塔函数，在包含随机发生的信号量化和缺失测量的统一框架中表征不完整测量的现象。基于不完整测量的可用概率信息，提出了一种新的滤波补偿方案，以确保对于所有随机不确定性、不完整测量和滤波器增益的随机扰动，保证滤波误差协方差的上限，并通过在每个采样时刻适当设计滤波器增益来最小化该上限。结果表明，通过求解两个Riccati类差分方程可以获得所需的滤波器增益，并且所提出的滤波算法是递归形式的，适合在线应用。最后，提供了一个说明性示例来证明所开发的过滤方法的可行性。
In this paper, the non-fragile recursive filtering problem is investigated for a class of networked time-varying nonlinear systems with stochastic uncertainties and incomplete measurements. By employing a stochastic Kronecker delta function, the phenomena of the incomplete measurements are characterized in a unified framework which contain the randomly occurring signal quantization and the missing measurements. Based on the available probability information of the incomplete measurements, a new filtering compensation scheme is proposed to ensure that, for all stochastic uncertainties, incomplete measurements and stochastic perturbations of the filter gain, an upper bound of the filtering error covariance is guaranteed and such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter gain can be obtained by solving two Riccati-like difference equations, and the proposed filtering algorithm is of a recursive form which is suitable for online applications. Finally, an illustrative example is provided to demonstrate the feasibility of the developed filtering approach.