Quantized Fault Detection with Mixed Time-Delays and Packet Dropouts

2013 ◽  
pp. 139-170
Keyword(s):  
Fault Detection ◽  
Time Delays ◽  
Packet Dropouts ◽  
Mixed Time Delays

scholarly journals Fault Detection Filter Design for Stochastic Systems with Mixed Time-Delays and Parameter Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Liyuan Hou ◽  
Shouming Zhong ◽  
Hong Zhu ◽  
Yong Zeng ◽  
Lin Shi
Keyword(s):  
Fault Detection ◽  
Time Delays ◽  
Stochastic Systems ◽  
Filter Design ◽  
Main Idea ◽  
Linear Matrix ◽  
Parameter Uncertainties ◽  
Fault Detection Filter ◽  
Mixed Time Delays ◽  
Detection Filter

This paper purposes the design of a fault detection filter for stochastic systems with mixed time-delays and parameter uncertainties. The main idea is to construct some new Lyapunov functional for the fault detection dynamics. A new robustly asymptotically stable criterion for the systems is derived through linear matrix inequality (LMI) by introducing a comprehensive different Lyapunov-Krasovskii functional. Then, the fault detection filter is designed in terms of linear matrix inequalities (LMIs) which can be easily checked in practice. At the same time, the error between the residual signal and the fault signal is made as small as possible. Finally, an example is given to illustrate the effectiveness and advantages of the proposed results.

scholarly journals Synchronization of Neural Networks with Mixed Time Delays under Information Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Dedong Yang ◽  
He-Xu Sun ◽  
Peng Yang ◽  
Tai-Hang Du
Keyword(s):  
Neural Networks ◽  
Hybrid Systems ◽  
Time Delays ◽  
Sufficient Condition ◽  
Packet Dropouts ◽  
Network Characteristics ◽  
Numerical Example ◽  
Mixed Time Delays ◽  
Synchronization Problem ◽  
Synchronization Scheme

This paper investigates the synchronization problem of neural networks with mixed time delays under information constrains. The designed synchronization scheme is built on the framework of hybrid systems. Besides including nonuniform sampling, some other characteristics, such as quantization, transmission-induced delays, and data packet dropouts, are also considered. The sufficient condition that depended on network characteristics is obtained to guarantee the remote asymptotical synchronization of neural networks with mixed time delays. A numerical example is given to illustrate the validity of the proposed method.

FAULT DETECTION IN MULTIRATE SAMPLED-DATA SYSTEMS WITH TIME-DELAYS

2002 ◽  
Vol 35 (1) ◽  
pp. 311-316 ◽  
Author(s):  
P. Zhang ◽  
S.X. Ding ◽  
G.Z. Wang ◽  
D.H. Zhou
Keyword(s):  
Fault Detection ◽  
Time Delays ◽  
Data Systems ◽  
Sampled Data
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