Dynamic Evolution Analysis Of Nonlinear Systems With Parametric Uncertainty

In recent years,nonlinear systems have attracted the attention of a large number of scholars because of their applications in biology,physics,engineering and so on.In addition,practical systems usually encounter model uncertainties,which may have a negative impact on system performance.Therefore,it is very significance to study the dynamic properties of nonlinear systems with parameter uncertainties.This paper mainly studies several kinds of nonlinear systems with parameter uncertainties.The dynamic characteristics of nonlinear systems with parameter uncertainties are studied.By using Gronwall-inequality and matrix inequality techniques,combining with stochastic nonlinear systems,multiple neural networks and fuzzy-logic systems,respectively,some theoretical criteria of evolutionary behavior are obtained.The main contents of this paper are introduced as follow:The stabilization of a class of stochastic nonlinear delay systems with parametric uncertainty is studied.By establishing the event-triggered mechanism and using the matrix inequality technique,an appropriate controller is designed to ensure the stabilization of stochastic nonlinear systems.On this basis,some sufficient conditions for the stabilization of the system are given.In addition,the lower bounds of sampling time intervals are also obtained by the established event-triggered mechanism.Global robust exponential synchronization for a class of multiple neural networks with parameter uncertainties and time delays is investigated.By designing the event-triggered controller and using the matrix inequality technique,several sufficient criteria are obtained to ensure global robust exponential synchronization of coupling neural networks.In particular,the coupling matrix does not need to be the Laplace matrix.In addition,the lower bounds of sampling time intervals are also found by the designed the event-triggered controller.The observer design and H_? algorithm are proposed for a class of discrete-time fuzzy-logic systems.The considered fuzzy-logic systems are subject to parameter uncertainty and unmeasurable state variables.To appropriately deal with parameter uncertainty and unmeasurable state variables,First,the space of premise variable is disintegrated.Then,the total partitioned regions will be divided into crisp regions and fuzzy regions.Finally,with the aid of partitioned regions,piecewise fuzzy H_? observers are presented.Hence,the augmented systems achieve the performance conditions: asymptotic convergence and H_? performance.