Impulsive effect on exponential synchronization of neural networks with leakage delay under sampled-data feedback control

2014 ◽  
Vol 23 (7) ◽  
pp. 070205 ◽  
Author(s):  
S. Lakshmanan ◽  
Ju H. Park ◽  
Fathalla A. Rihan ◽  
R. Rakkiyappan
Keyword(s):  
Neural Networks ◽  
Feedback Control ◽  
Exponential Synchronization ◽  
Leakage Delay ◽  
Impulsive Effect ◽  
Sampled Data ◽  
Data Feedback

Time-Delayed Sampled-Data Feedback Control of Differential Systems Undergoing Hopf Bifurcation

2021 ◽  
Vol 31 (01) ◽  
pp. 2150004
Author(s):  
Huan Su ◽  
Jing Xu
Keyword(s):  
Hopf Bifurcation ◽  
Feedback Control ◽  
Blood Transfusion ◽  
Sampling Period ◽  
Delayed Feedback Control ◽  
Control Technique ◽  
Blood Collection ◽  
Sampled Data ◽  
Differential Systems ◽  
Data Feedback

In this paper, time-delayed sampled-data feedback control technique is used to asymptotically stabilize a class of unstable delayed differential systems. Through the analysis for the distribution change of eigenvalues, an effective interval of the control parameter is obtained for a given sampling period. Here an indirect strategy is taken. Specifically, the system of continuous-time delayed feedback control is studied first by Hopf bifurcation theory. And then, the result and implicit function theorem are used to analyze the system of time-delayed sampled-data feedback control with a sufficiently small sampling period. Considering the practical criterion for the size of sampling period, the upper bound of sampling period is estimated. Finally, an application example, an unstable Mackey–Glass model, is asymptotically stabilized by introducing a blood transfusion item with time-delayed sampled-data feedback control. The blood transfusion speed and blood collection test period are derived from the main results. Some simulations and comparisons show the correctness and advantages of the main theoretical results.

Reduction of magnetic field fluctuations in powered magnets for NMR using inductive measurements and sampled-data feedback control

2011 ◽  
Vol 212 (2) ◽  
pp. 254-264 ◽  
Author(s):  
Mingzhou Li ◽  
Jeffrey L. Schiano ◽  
Jenna E. Samra ◽  
Kiran K. Shetty ◽  
William W. Brey
Keyword(s):  
Magnetic Field ◽  
Feedback Control ◽  
Sampled Data ◽  
Data Feedback ◽  
Field Fluctuations

scholarly journals Exponential Synchronization for Stochastic Neural Networks with Mixed Time Delays and Markovian Jump Parameters via Sampled Data

2014 ◽  
Vol 2014 ◽  
pp. 1-17
Author(s):  
Yingwei Li ◽  
Xueqing Guo
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Exponential Synchronization ◽  
Stochastic Neural Networks ◽  
Sampled Data ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Mixed Time Delays ◽  
Data Controller

The exponential synchronization issue for stochastic neural networks (SNNs) with mixed time delays and Markovian jump parameters using sampled-data controller is investigated. Based on a novel Lyapunov-Krasovskii functional, stochastic analysis theory, and linear matrix inequality (LMI) approach, we derived some novel sufficient conditions that guarantee that the master systems exponentially synchronize with the slave systems. The design method of the desired sampled-data controller is also proposed. To reflect the most dynamical behaviors of the system, both Markovian jump parameters and stochastic disturbance are considered, where stochastic disturbances are given in the form of a Brownian motion. The results obtained in this paper are a little conservative comparing the previous results in the literature. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

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