Exponential H∞ filtering for nonlinear discrete-time switched stochastic systems with mixed time delays and random missing measurements

2011 ◽  
Vol 14 (3) ◽  
pp. 807-816 ◽  
Author(s):  
Dan Zhang ◽  
Li Yu ◽  
Wen-An Zhang
Keyword(s):  
Discrete Time ◽  
Time Delays ◽  
Stochastic Systems ◽  
Missing Measurements ◽  
Mixed Time Delays ◽  
Switched Stochastic Systems

H∞ Dynamic Output Feedback Control for a Class of Discrete-Time Stochastic Systems with Sector Nonlinearities and Mixed Time-Delays

2012 ◽  
Vol 490-495 ◽  
pp. 296-300
Author(s):  
Wei Jun Ma ◽  
Yan Mei Hu ◽  
Xian Zhang
Keyword(s):  
Feedback Control ◽  
Discrete Time ◽  
Output Feedback ◽  
Time Delays ◽  
Stochastic Systems ◽  
Output Feedback Control ◽  
Dynamic Output Feedback ◽  
Mixed Time Delays ◽  
Dynamic Output ◽  
Sector Nonlinearities

This paper deals with the dynamic output feedback control problem for a class of discrete-time stochastic systems involving sector nonlinearities and mixed time-delays. Based on the control theory and Lyapunov-Krasovskii functional method, an effective approach is proposed to design a dynamic output feedback controller such that the resultant closed-loop system is stochastically stable with a prescribed disturbance attention in the mean square sense and has a performance level. A numerical example demonstrates the effectiveness of the method.

Stability analysis for discrete-time stochastic neural networks with mixed time delays and impulsive effects

2010 ◽  
Vol 88 (12) ◽  
pp. 885-898 ◽  
Author(s):  
R. Raja ◽  
R. Sakthivel ◽  
S. Marshal Anthoni
Keyword(s):  
Neural Networks ◽  
Equilibrium Point ◽  
Discrete Time ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Impulsive Effects ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Mixed Time Delays ◽  
The Stability

This paper investigates the stability issues for a class of discrete-time stochastic neural networks with mixed time delays and impulsive effects. By constructing a new Lyapunov–Krasovskii functional and combining with the linear matrix inequality (LMI) approach, a novel set of sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point for the addressed discrete-time neural networks. Then the result is extended to address the problem of robust stability of uncertain discrete-time stochastic neural networks with impulsive effects. One important feature in this paper is that the stability of the equilibrium point is proved under mild conditions on the activation functions, and it is not required to be differentiable or strictly monotonic. In addition, two numerical examples are provided to show the effectiveness of the proposed method, while being less conservative.

On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements

2008 ◽  
Vol 53 (9) ◽  
pp. 2170-2180 ◽  
Author(s):  
Bo Shen ◽  
Zidong Wang ◽  
Huisheng Shu ◽  
Guoliang Wei
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Missing Measurements

Robust stabilization for discrete-time nonlinear singular systems with mixed time delays

2011 ◽  
Vol 14 (5) ◽  
pp. 1411-1421 ◽  
Author(s):  
Jianning Li ◽  
Hongye Su ◽  
Zhengguang Wu ◽  
Jian Chu
Keyword(s):  
Discrete Time ◽  
Time Delays ◽  
Singular Systems ◽  
Robust Stabilization ◽  
Nonlinear Singular Systems ◽  
Mixed Time Delays
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