估计员
马尔可夫链
国家(计算机科学)
采样(信号处理)
理论(学习稳定性)
应用数学
马尔可夫过程
数学
系列(地层学)
计算机科学
基质(化学分析)
算法
离散数学
数学优化
统计
机器学习
生物
滤波器(信号处理)
计算机视觉
古生物学
复合材料
材料科学
标识
DOI:10.23919/ccc58697.2023.10239815
摘要
This article investigates the $H_{\infty}$ state estimation issue for a class of complex networks subject to state saturations. A Markov chain is introduced to model the nonuniform sampling of the measurement output. With aim at estimating the network state, a series of state estimators are constructed in light of the nonuniformly sampled measurements sent by sensors. By employing the Lyapunov stability method, a sufficient condition is presented to ensure that the resulting error system is stochastically stable and the prescribed $H_{\infty}$ performance index is also satisfied. Subsequently, the gain matrices of the expected estimator are obtained by solving certain matrix inequalities. Finally, a simulation example is given to show the state estimation method's effectiveness.
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