研究一类具有随机采样特性的网络化系统H∞滤波问题.通过将传感器的随机采样过程建模成马尔可夫链,将数据量化作用转化为模型的参数不确定性,并用二值随机变量描述丢包过程,从而用一个多随机变量的马尔可夫不确定性模型来描述滤波误差系统.应用Lyapunov稳定性理论和随机系统分析方法,导出了滤波误差系统随机稳定且具有给定H∞性能的充分条件,并给出了滤波器的设计方法.仿真结果验证了所提出方法的有效性. A class of H∞ filtering problem for networked systems with stochastic sampling is investigated. By modeling the sensor’s random sampling process into a Markov chain, the quantization of data is transformed into the parametric uncertainty of the model. The binary random variable The process of packet loss, so a Markov uncertainty model with multiple random variables is used to describe the filtering error system. The Lyapunov stability theory and the stochastic system analysis method are used to derive the filtering error system stochastically and with a given H∞ performance Sufficient conditions are given and the design method of the filter is given.The simulation results verify the effectiveness of the proposed method.