具有随机采样特性的网络化系统H∞ 滤波

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.

H∞ filtering for a class of networked systems with stochastic sampling

The H_∞  filtering problem is considered for a class of networked systems with stochastic sampling. The stochastic sampling process of sensors is described as a Markov chain, the effect of signal quantization is transformed into the parameter uncertainty of the model, then a set of binary variables are introduced to model the random packet loss process, thus a uncertain Markovian model with multiple random variables is obtained to describe the resulting filtering error system. Based on the Lyapunov stability theory and stochastic analysis method, some conditions are derived such that the filtering error system is stochastic stable and achieves a prescribed H_∞ performance, the design procedure of the filter is also provided. A simulation is given to demonstrate the effectiveness of the proposed results.