scholarly journals Robust $H_{\infty}$ Control of Lurie Nonlinear Stochastic Network Control Systems With Multiple Additive Time-Varying Delay Components

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 3390-3405 ◽  
Author(s):  
Hongqian Lu ◽  
Chaoqun Guo ◽  
Yue Hu
Keyword(s):  
Control Systems ◽  
Network Control ◽  
Stochastic Network ◽  
Time Varying ◽  
Time Varying Delay ◽  
Network Control Systems ◽  
Varying Delay

Output Feedback Stabilization of Networked Control Systems with Time-Varying Delay

2013 ◽  
Vol 718-720 ◽  
pp. 1836-1841
Author(s):  
Xue Feng Zhang ◽  
Yan Xia Shen ◽  
Hua Ping Zhang
Keyword(s):  
Control Systems ◽  
Output Feedback ◽  
Networked Control Systems ◽  
Networked Control System ◽  
Feedback Stabilization ◽  
Networked Control ◽  
Time Varying ◽  
Time Varying Delay ◽  
Output Feedback Stabilization ◽  
Varying Delay

The problem of output feedback stabilization for networked control system with time-varying delay is discussed. By constructing a novel LyapunovKrasovskii functional,the output feedback controller is obtained in terms of LMIs. The dynamics information of the time-varying delay is taken into account in the structured controller, and the obtained result is less conservative. A numerical example is given to illustrate the effectiveness of the presented method.

scholarly journals Research on Adaptive Neural Network Control System Based on Nonlinear U-Model with Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Fengxia Xu ◽  
Yao Cheng ◽  
Hongliang Ren ◽  
Shili Wang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Delay System ◽  
Network Control ◽  
Identification Algorithm ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Time Varying Delay ◽  
Adaptive Neural Network Control ◽  
Varying Delay

U-model can approximate a large class of smooth nonlinear time-varying delay system to any accuracy by using time-varying delay parameters polynomial. This paper proposes a new approach, namely, U-model approach, to solving the problems of analysis and synthesis for nonlinear systems. Based on the idea of discrete-time U-model with time-varying delay, the identification algorithm of adaptive neural network is given for the nonlinear model. Then, the controller is designed by using the Newton-Raphson formula and the stability analysis is given for the closed-loop nonlinear systems. Finally, illustrative examples are given to show the validity and applicability of the obtained results.

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