Multidimensional Systems and Signal Processing

, Volume 25, Issue 1, pp 157–177

Robust state estimation for two-dimensional stochastic time-delay systems with missing measurements and sensor saturation

Authors

    • Department of MathematicsSoutheast University
  • Zidong Wang
    • Department of Information Systems and ComputingBrunel University
  • Xiaohui Liu
    • Department of Information Systems and ComputingBrunel University
Article

DOI: 10.1007/s11045-012-0197-0

Cite this article as:
Liang, J., Wang, Z. & Liu, X. Multidim Syst Sign Process (2014) 25: 157. doi:10.1007/s11045-012-0197-0

Abstract

In this paper, the robust state estimation problem is investigated for a class of uncertain two-dimensional (2-D) systems with state delays and stochastic disturbances. The imperfect measurement output is subject to probabilistic data missing and sensor saturations. The missing phenomenon of the sensor measurement is governed by a stochastic variable satisfying the Bernoulli random binary distribution law, and the sensor saturation is considered to reflect the limited capacity of the communication networks. The parameter uncertainties are assumed to be norm-bounded and enter into the linear part of the system model in both directions. Through available but imperfect output measurements, a state estimator is designed to estimate the system states in the presence of data missing, sensor saturation, parameter uncertainties as well as stochastic perturbations. By defining an energy-like functional and conducting some stochastic analysis, several sufficient criteria in terms of matrix inequalities are established, which not only ensure the existence of the desired estimator gains but also guarantee the globally robustly asymptotic stability in the mean square of the estimation error dynamics. Finally, two numerical examples are exploited to show the effectiveness of the design method proposed in this paper.

Keywords

Two-dimensional (2-D) systemsState estimationMissing measurementsSensor saturationParameter uncertaintiesStochastic disturbances

Copyright information

© Springer Science+Business Media, LLC 2012