一类具有时变时滞的中立型系统的稳定性分析

本文研究一类具有状态时滞和输入时滞的时变时滞线性中立型系统.首先,通过选取合适的Lya-punov-Krasovskii泛函,应用LMI方法和Lyapunov-Krasovskii稳定性定理对时滞相关的系统进行稳定性分析,并设计了相应的控制器.改进了时不变时滞线性系统方面的一些结果.最后用实例验证所得到结果.     

应用非线性规划辨识线性系统

本文研究了非线性规划在设计线性系统辨识器中的应用,文中讨论了集中参数系统的辨识和具有未知时滞线性系统的辨识,采用多输出误差二次型目标函数,引入了时变非线性规划的新概念。为保证时滞线性系统的辨识过程是收敛的,引入了逻辑决策。文中提出的设计方案保证参数快速收敛到真值。     

分数阶时变广义系统稳定性分析

基于Caputo分数阶导数,研究了分数阶时变广义线性系统和分数阶时变广义非线性系统的稳定性问题.首先利用相关不等式,给出了一个时变广义线性系统无脉冲且稳定的充分条件.然后,通过慢子系统来判断快子系统的变化,并利用Riccati方程,建立了分数阶时变广义非线性系统是渐近稳定的判定准则.最后,给出了算例和Simulink仿真结果,以说明结论的正确性.     

时滞广义时变系统的容许性分析与镇定

研究了时滞广义时变系统的容许性与镇定性问题.首先,基于广义Lyapunov不等式、线性矩阵不等式和受限等价方法,建立时滞广义时变系统的Lyapunov不等式,将时滞广义时变系统的容许性问题转化为求解时滞广义时变系统的Lyapunov不等式问题,得到了系统容许的充分条件.然后,根据充分条件进一步研究了时滞广义时变系统的镇定问题,给出了状态反馈镇定器的设计方法.最后,通过数值算例验证了所得结论的有效性.     

一类单时滞线性系统的模糊变结构控制

研究了一类单时滞线性系统变结构控制律的设计方法,基于模糊T-S模型把一类单时滞线性系统化为若干个时滞子系统,然后对时滞子系统设计变结构控制律,取全局控制作为系统的控制律,从而达到对单时滞线性系统系统进行控制的目的,给出了单时滞系统的滑模稳定的条件.仿真表明控制策略的有效性.     

一类具有时变时滞的神经网络的稳定性分析

研究了一类具有时变时滞的神经网络的稳定性,首先将要研究的模型转化为数学系统模型,利用Lyapunov-Krasovskii稳定性理论和线性矩阵不等式技术(LMI方法),精炼和推广了一些已有的结果,所得结果为时变时滞神经网络提供了新的稳定性判定准则.     

具有时变时滞的不确定离散广义系统的稳定与镇定

本文主要研究具有时变时滞的不确定离散广义系统的稳定性与镇定.利用线性矩阵不等式方法,给出了具有时变时滞的离散广义系统稳定的充分条件,推广了历算广义系统稳定与镇定的相关结果.     

无界变时滞神经网络全局稳定性

该文研究了具无界变时滞的时变神经网络的全局稳定性.利用两种不同的分析方法得到了保证这类神经网络全局渐近稳定的一些充分条件.推广和改进了现有文献中常时滞或时滞为零的相应结果.     

具有非线性扰动的不确定随机时变时滞系统的鲁棒镇定

本文研究了具有非线性扰动的不确定随机时变时滞系统的鲁棒镇定的问题.构造了适当的Lyapunov-Krasovskii泛函并利用自由权矩阵方法,借助于线性矩阵不等式(LMI)技术,设计了一个无记忆状态反馈控制器,并获得了不确定随机时变时滞系统的时滞依赖鲁棒镇定判据.数值例子及其仿真曲线表明所提出的理论结果是有效的.     

中立型时滞系统的稳定性改进判据

研究了含有时变时滞的不确定中立系统的时滞相关稳定性问题.假定不确定参数是范数有界的,通过构造新的Lyapunov泛函和使用更一般的时滞分解方法,得到了基于LMI新颖的时滞相关稳定条件,并且用Matlab LMI工具箱很容易地求解.数值实例表明本文方法所得结果优于现有文献中的结果.     

Dynamics of fuzzy impulsive bidirectional associative memory neural networks with time-varying delays

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy bidirectional associative memory (BAM) neural networks with impulsive effect and time-varying delays is investigated. The model of fuzzy impulsive BAM neural networks with time-varying delays established as a modified TS fuzzy model is new in which the consequent parts are composed of a set of impulsive BAM neural networks with time-varying delays. Further the exponential stability for fuzzy impulsive BAM neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique without tuning any parameters. In addition, an example is provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.     

Synchronization analysis in an array of asymmetric neural networks with time-varying delays and nonlinear coupling

In this paper, the global exponential synchronization is investigated for an array of asymmetric neural networks with time-varying delays and nonlinear coupling, assuming neither the differentiability for time-varying delays nor the symmetry for the inner coupling matrices. By employing a new Lyapunov-Krasovskii functional, applying the theory of Kronecker product of matrices and the technique of linear matrix inequality (LMI), a delay-dependent sufficient condition in LMIs form for checking global exponential synchronization is obtained. The proposed result generalizes and improves the earlier publications. An example with chaotic nodes is given to show the effectiveness of the obtained result.     

Delay-dependent stability of neutral systems with time-varying delays using delay-decomposition approach

This paper is concerned with the problem of asymptotic stability of neutral systems. A new delay-dependent stability condition is derived in terms of linear matrix inequality to ensure a large upper bound of the time-delay by non-uniformly dividing the delay interval into multiple segments. A new Lyapunov-Krasovskii functional is constructed with different weighting matrices corresponding to different segments in the Lyapunov-Krasovskii functional, where both constant time delays and time-varying delays have been taken into account. Numerical examples are given to demonstrate the effectiveness and less conservativeness of the proposed methods.     

Exponential stability and applications of switched positive linear impulsive systems with time-varying delays and all unstable subsystems

The global uniform exponential stability of switched positive linear impulsive systems with time-varying delays and all unstable subsystems is studied in this paper, which includes two types of distributed time-varying delays and discrete time-varying delays. Switching behaviors dominating the switched systems can be either stabilizing and destabilizing in the new designed switching sequence. We design new linear programming algorithm process to find the feasible ratio of stabilizing switching behaviors, which can be compensated by unstable subsystems, destabilizing switching behaviors, and impulses. Specically, we add a kind of nonnegative impulses which is consistent with the switching behaviors for the systems. Employing a multiple co-positive Lyapunov-Krasovskii functional, we present several new sufficient stability criteria and design new switching sequence. Then, we apply the obtained stability criteria to the exponential consensus of linear delayed multi-agent systems, and obtain the new exponential consensus criteria. Three simulations are provided to demonstrate the proposed stability criteria.     

Exponential stability results for uncertain neutral systems with interval time-varying delays and Markovian jumping parameters

This paper deals with the global exponential stability analysis of neutral systems with Markovian jumping parameters and interval time-varying delays. The time-varying delay is assumed to belong to an interval, which means that the lower and upper bounds of interval time-varying delays are available. A new global exponential stability condition is derived in terms of linear matrix inequality (LMI) by constructing new Lyapunov-Krasovskii functionals via generalized eigenvalue problems (GEVPs). The stability criteria are formulated in the form of LMIs, which can be easily checked in practice by Matlab LMI control toolbox. Two numerical examples are given to demonstrate the effectiveness and less conservativeness of the proposed methods.     

Robust exponential stability and stabilizability of linear parameter dependent systems with delays

The robust exponential stability and stabilizability problems are addressed in this paper for a class of linear parameter dependent systems with interval time-varying and constant delays. In this paper, restrictions on the derivative of the time-varying delay is not required which allows the time-delay to be a fast time-varying function. Based on the Lyapunov-Krasovskii theory, we derive delay-dependent exponential stability and stabilizability conditions in terms of linear matrix inequalities (LMIs) which can be solved by various available algorithms. Numerical examples are given to illustrate the effectiveness of our theoretical results.     

Synchronization of singular complex dynamical networks with time-varying delays

This paper considers delay dependent synchronizations of singular complex dynamical networks with time-varying delays. A modified Lyapunov-Krasovskii functional is used to derive a sufficient condition for synchronization in terms of LMIs (linear matrix inequalities) which can be easily solved by various convex optimization algorithms. Numerical examples show the effectiveness of the proposed method.     

基于LMI方法的多时滞随机神经网络的指数稳定性

研究了一类具有多个时滞的随机神经网络的均方指数稳定性问题,应用Lyapunov-Krasovskii泛函稳定理论和线性矩阵不等式(LMI)方法,建立了该系统解的指数稳定判别准则,最后通过数值举例阐述了结果的有效性.     

分布时滞系统的动态输出反馈鲁棒稳定

主要研究了一类具分布时变时滞不确定系统的输出反馈鲁棒稳定问题.基于动态输出反馈和Lyapunov-Krasovskii泛函,给出了闭环系统与时滞相关的鲁棒稳定的条件.所得条件为线性矩阵不等式形式,便于运用内点算法进行求解.     

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.