Non-fragile H∞ state estimation for nonlinear networked system with probabilistic diverging disturbance and multiple missing measurements

作者：Xie, Linghua; Wang, Yan; Yang, Yongqing^*; Li, Li

来源：Neurocomputing, 2017, 230: 270-278.

DOI：10.1016/j.neucom.2016.12.018

• H infinity state estimator
• Probabilistic diverging disturbance
• Multiple missing measurements
• Non-fragile filter

This paper is concerned with the non-fragile H-infinity state estimation problem for a class of discrete-time networked system with probabilistic diverging disturbance and multiple missing measurements. The measurement missing phenomenon is assumed to occur randomly and the missing probability for each sensor is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [0, 1]. The aim of this paper is to estimate the networked system by designing a non-fragile H-infinity estimator such that the augmented estimation error system is asymptotically mean square stable with a prescribed H-infinity disturbance attention level gamma. By using the Lyapunov method and stochastic analysis, we derive a sufficient condition for the existence of the desired estimator. By solving the linear matrix inequalities (LMIs), the estimator gain matrix is given. Two numerical examples are employed to demonstrate the effectiveness and applicability of the proposed design technique.

• 出版日期2017-3-22
• 单位江南大学