Delay-dependent robustH ∞ control for uncertain singular time-delay system with Markovian jumping parameters

2012 ◽  
Vol 34 (3) ◽  
pp. 296-307 ◽  
Author(s):  
Hongqian Lu ◽  
Wuneng Zhou ◽  
Chunmei Duan ◽  
Xingguang Qi
Keyword(s):  
Time Delay ◽  
Delay System ◽  
Markovian Jumping Parameters ◽  
Time Delay System ◽  
Markovian Jumping ◽  
Delay Dependent ◽  
Singular Time

Delay-Dependent Stabilization for Singular Time-Delay Systems

2012 ◽  
Vol 182-183 ◽  
pp. 1255-1259 ◽  
Author(s):  
Jin Feng Gao ◽  
Jia Ren ◽  
Chuang Meng
Keyword(s):  
Time Delay ◽  
Delay Interval ◽  
Energy Function ◽  
Delay System ◽  
Delay Systems ◽  
Time Delay Systems ◽  
Time Delay System ◽  
Delay Dependent ◽  
Singular Time

Some new results of delay-dependent stabilization for linear singular time-delay systems are presented. And the time delay considered here is assumed to be constant but unknown. By using a new Lyapunov-krasovskii functional which splits the whole delay interval into two subintervals and defines a different energy function on each subinterval, a sufficient delay-dependent condition is obtained for the singular time-delay system to be regular, impulse free and stable.

scholarly journals RobustH∞Filtering for Uncertain Neutral Stochastic Systems with Markovian Jumping Parameters and Time Delay

2015 ◽  
Vol 2015 ◽  
pp. 1-16
Author(s):  
Yajun Li ◽  
Zhaowen Huang
Keyword(s):  
Time Delay ◽  
Stochastic Systems ◽  
Filter Design ◽  
Linear Matrix ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Stochastically Stable ◽  
Neutral Stochastic Systems ◽  
Error System ◽  
Delay Dependent

This paper deals with the robustH∞filter design problem for a class of uncertain neutral stochastic systems with Markovian jumping parameters and time delay. Based on the Lyapunov-Krasovskii theory and generalized Finsler Lemma, a delay-dependent stability condition is proposed to ensure not only that the filter error system is robustly stochastically stable but also that a prescribedH∞performance level is satisfied for all admissible uncertainties. All obtained results are expressed in terms of linear matrix inequalities which can be easily solved by MATLAB LMI toolbox. Numerical examples are given to show that the results obtained are both less conservative and less complicated in computation.

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