Discrete-Time Stability of a Class of Hermitian Polynomial Matrices with Positive Semidefinite Coefficients

2010 ◽  
pp. 409-414
Author(s):  
Harald K. Wimmer
Keyword(s):  
Discrete Time ◽  
Positive Semidefinite ◽  
Time Stability ◽  
Polynomial Matrices ◽  
Hermitian Polynomial

Finite-Time H∞ Control of one Family of Fuzzy Discrete-Time System with Norm-Bounded Disturbance

2011 ◽  
Vol 225-226 ◽  
pp. 428-432 ◽  
Author(s):  
Cai Xia Liu ◽  
Ying Qi Zhang
Keyword(s):  
Discrete Time ◽  
Finite Time ◽  
Function Theory ◽  
Matrix Inequalities ◽  
Time Stability ◽  
Discrete Time System ◽  
Time System ◽  
Finite Time Stability ◽  
Time Control ◽  
Bounded Disturbance

This paper deals with finite-time control problem of a class of fuzzy discrete-time system with time-varying norm-bounded disturbance. Applying the Lyapunov function theory and matrix inequalities, a sufficient condition is obtained for robust finite-time stability and the fuzzy system satisfies a prescribed level for the effect of the disturbance input on the controlled output.

scholarly journals Neural Network Inverse Model Control Strategy: Discrete-Time Stability Analysis for Relative Order Two Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
M. A. Hussain ◽  
Jarinah Mohd Ali ◽  
M. J. H. Khan
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Discrete Time ◽  
Control Strategy ◽  
Direct Method ◽  
Time Derivative ◽  
Inverse Model ◽  
Relative Order ◽  
Time Stability ◽  
Model Control

This paper discusses the discrete-time stability analysis of a neural network inverse model control strategy for a relative order two nonlinear system. The analysis is done by representing the closed loop system in state space format and then analyzing the time derivative of the state trajectory using Lyapunov’s direct method. The analysis shows that the tracking output error of the states is confined to a ball in the neighborhood of the equilibrium point where the size of the ball is partly dependent on the accuracy of the neural network model acting as the controller. Simulation studies on the two-tank-in-series system were done to complement the stability analysis and to demonstrate some salient results of the study.

Finite-time stability analysis of discrete-time fuzzy Hopfield neural network

2015 ◽  
Vol 159 ◽  
pp. 263-267 ◽  
Author(s):  
Jianjun Bai ◽  
Renquan Lu ◽  
Anke Xue ◽  
Qingshan She ◽  
Zhonghua Shi
Keyword(s):  
Neural Network ◽  
Stability Analysis ◽  
Discrete Time ◽  
Finite Time ◽  
Hopfield Neural Network ◽  
Time Stability ◽  
Finite Time Stability
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