Robust H∞ filtering for a class of complex networks with stochastic packet dropouts and time delays.

Jie Zhang,Pengfei Guo,Hamid Reza Karimi,Ming Lyu,Yuming Bo

doi:10.1155/2014/560234

Abstract

The robust H ∞ filtering problem is investigated for a class of complex network systems which has stochastic packet dropouts and time delays, combined with disturbance inputs. The packet dropout phenomenon occurs in a random way and the occurrence probability for each measurement output node is governed by an individual random variable. Besides, the time delay phenomenon is assumed to occur in a nonlinear vector-valued function. We aim to design a filter such that the estimation error converges to zero exponentially in the mean square, while the disturbance rejection attenuation is constrained to a given level by means of the H ∞ performance index. By constructing the proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the state detection observer for the discrete systems, and the observer gain is also derived by solving linear matrix inequalities. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

Highlights

The H∞ filtering problem has drawn particular attention, since H∞ filters are insensitive to the exact knowledge of the statistics of the noise signals
In [10], a model of multiple missing measurements has been presented by using a diagonal matrix to account for the different missing probabilities for individual sensors
Our aim in this paper is to develop techniques to deal with the robust H∞ filtering problem for a class of complex systems with stochastic packet dropouts, time delays, and disturbance inputs

Summary

Introduction

The H∞ filtering problem has drawn particular attention, since H∞ filters are insensitive to the exact knowledge of the statistics of the noise signals. In the past few years, the filtering problem with missing measurements has received much attention [10,11,12,13,14,15,16,17]. In [10], a model of multiple missing measurements has been presented by using a diagonal matrix to account for the different missing probabilities for individual sensors. The optimal filter design problem has been tackled in [13] for systems with multiple packet dropouts by solving a recursive difference equation (RDE)

Objectives

Results

Conclusion