Robust control of a class of uncertain nonlinear systems
Systems & Control Letters
A constructive approach to stabilizability and stabilization of a class of nDD Systems
Multidimensional Systems and Signal Processing
H∞ and Robust Control of 2-D Systems in FM Second Model
Multidimensional Systems and Signal Processing
Stability and Stabilization of Uncertain 2-D Discrete Systems with Stochastic Perturbation
Multidimensional Systems and Signal Processing
Filtering for uncertain 2-D discrete systems with state delays
Signal Processing
Brief paper: H2 and mixed H2/H∞ control of two-dimensional systems in Roesser model
Automatica (Journal of IFAC)
Brief paper: Set-membership filtering for systems with sensor saturation
Automatica (Journal of IFAC)
Brief paper: Robust fault detection for networked systems with communication delay and data missing
Automatica (Journal of IFAC)
IEEE Transactions on Signal Processing
Stabilization of 2D saturated systems by state feedback control
Multidimensional Systems and Signal Processing
IEEE Transactions on Signal Processing
Digital Signal Processing
LMI Stability Tests for the Fornasini-Marchesini Model
IEEE Transactions on Signal Processing - Part II
H∞ filtering of 2-D discrete systems
IEEE Transactions on Signal Processing
Brief H∞ control and robust stabilization of two-dimensional systems in Roesser models
Automatica (Journal of IFAC)
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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.