时变线性分布参数系统的鲁棒指数稳定性分析

研究了一类不确定性分布参数系统的鲁棒指数稳定性和稳定化问题.利用推广到Hilbert 空间的Lyapunov-Krasovskii方法和不等式技巧,证明了线性时滞系统的鲁棒指数稳定性,并且依赖时滞的鲁棒指数稳定性和稳定化的充分条件可以表示成线性算子不等式(LOI)形式,其中决策变量是Hilbert空间的算子.把得到的结果应用到一个抛物型方程,这些条件归结为线性矩阵不等式(LMI).最后,一个数值例子说明了稳定性分析的有效性.     

灰色中立随机线性时滞系统的稳定性分析(英文)

通过使用灰色矩阵覆盖集的分解方法和矩阵范数的性质,构造李雅普诺夫函数,研究了灰色中立随机线性时滞系统的鲁棒稳定性和几乎指数鲁棒稳定性.     

具有分布时滞和区间参数的随机系统的p-阶矩指数鲁棒稳定性

研究了一类具有分布时滞和区间参数的随机系统的p-阶矩指数鲁棒稳定性问题,利用Liapunov-Krasovskii泛函、区间矩阵的分解技术及Ito公式,得到了该系统p-阶矩指数鲁棒稳定的时滞依赖的稳定性判据.通过数值例子说明了所得判据的有效性和实用性.     

鲁棒采样依赖指数稳定性分析新方法

提供了分析多边形不确定性周期采样系统鲁棒指数稳定性的新方法.首先,对确定性采样系统,构造了一个类Lyapunov泛函.该泛函引入了状态积分项而放松了对通常意义下Lyapunov泛函必须正定的要求.利用该泛函以及给出的关于线性采样系统指数稳定的新引理,结合改进的Wirtinger积分不等式,导出了采样依赖指数稳定性准则.然后,该准则被推广到多边形不确定性周期采样系统,得到了鲁棒采样依赖指数稳定性准则.最后,举例说明了所得稳定性结果比现存的某些文献报道的结果保守性较小.     

具有脉冲效应的灰色随机时滞系统的鲁棒稳定性

研究了一类具有脉冲效应和时变时滞的灰色随机系统的鲁棒稳定性问题。在给出了脉冲随机泛函微分系统随机稳定性的条件的基础上,首先利用Lyapunov-KrasoVskii泛函法和灰矩阵的连续矩阵覆盖的分解技术,得到了具有脉冲效应和时变时滞的灰色随机系统的随机鲁棒稳定性判据,进而基于所得的这个随机鲁棒稳定性判据和Dini导数,给出了该系统指数鲁棒稳定性的判据。实例表明,所得判据是有效的和实用的。     

具有时变灰参数的随机时滞系统的p-阶矩指数鲁棒稳定性

利用灰矩阵的矩阵覆盖集的分解技术和Lyapunov函数法,研究了具有时变灰色参数的随机时滞系统的p-阶矩指数鲁棒稳定性问题,得到了该系统p-阶矩指数鲁棒稳定的时滞独立和时滞依赖的条件,并通过数值例子说明了判别条件的有效性和实用性.     

时滞不确定性Markov切换线性随机微分系统的指数鲁棒稳定性

研究了一类时滞不确定性Markov切换随机微分系统的均方指数鲁棒随机稳定性\bd 系统中的时滞是时变的, 不确定项结构为范数有界, Markov切换是连续时间、离散状态的时齐Markov过程{\bf\!.} 利用随机Lyapunov函数方法和LMI技术, 得到了几个判定系统均方指数鲁棒随机稳定性的充分性条件\bd 一个数值例子说明了判据的有效性和可行性.     

具有时滞和脉冲干扰的大系统鲁棒指数稳定性

利用向量Lyapunov函数法,研究了一类具有输入扰动、可变时滞和脉冲干扰的非线性大系统的鲁棒指数稳定性.在假定大系统的每个子系统都能被指数镇定,且闭环系统的稳定反馈和相应的Lyapunov函数已知的前提下,给出该大系统鲁棒指数稳定的充分条件,并得到系统的指数收敛率.最后用两个数值算例说明结论的正确性和有效性.     

时滞切换神经网络系统的鲁棒指数稳定性

研究了具有时间滞后切换不确定细胞神经网络(UCNNs)系统的指数稳定性.利用同胚映射和M矩阵理论,得到UCNNs系统平衡点存在性,唯一性和指数稳定性的充分条件;利用平均驻留时间方法,研究了时滞切换UCNNs系统限制切换下的鲁棒指数稳定性,并得到确保系统全局指数稳定的充分条件.     

中立型带跳不确定变时滞随机系统鲁棒指数稳定

研究了一类中立型Markov跳变随机系统鲁棒指数稳定性,借助于Lyapunov-Krasovskii 泛函方法和随机稳定性理论,给出并证明了使中立型Markov跳变时滞随机系统指数稳定的充分条件,所有结果以线性矩阵不等式形式给出,算例表明了所给出的稳定性判据的有效性.     

Robust exponential stabilization for large-scale uncertain impulsive systems with coupling time-delays

In this paper, we aim to study robust exponential stabilization for a large-scale uncertain impulsive system with coupling time-delays. Furthermore, we also provide an estimation of the rate of convergence of exponential stabilization. By utilizing the Lyapunov method and Razumikhin technique, we shall design the feedback hybrid controllers in terms of linear matrix inequalities under which the robust exponential stability is achieved for a closed-loop large-scale uncertain impulsive system with coupling time-delays. Moreover, we shall also use the results obtained to design impulsive controllers for a large-scale uncertain continuous system under which the closed-loop continuous system achieves robust and exponential stability. To illustrate our results, one example is solved.     

New results of global robust exponential stability of neural networks with delays

In this paper, the global robust exponential stability of interval neural networks with delays is investigated. Employing homeomorphism techniques and Lyapunov functions, we establish some sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point for delayed neural networks. It is shown that the obtained results improve and generalize the previously published results.     

Global robust exponential stability of delayed neural networks: An LMI approach

In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for neural networks with parametric uncertainties and time delay are studied. Based on Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique, some delay-dependent criteria are derived to guarantee global robust exponential stability. The exponential convergence rate can be easily estimated via these criteria.     

Robust exponential stability of discrete‐time uncertain impulsive neural networks with time‐varying delay

The purpose of this paper is to investigate the robust exponential stability of discrete‐time uncertain impulsive neural networks with time‐varying delay. By using Lyapunov functions together with Razumikhin technique, some new robust exponential stability criteria are presented. The obtained results show that the robust stability can be retained under certain impulsive perturbations for the neural network, which has the robust stability property. The obtained results also show that impulses can robustly stabilize the neural network, which does not have the robust stability property. Some examples, together with their simulations, are also given to show the effectiveness and the advantage of the presented results. It should be noted that the impulsive robust exponential stabilization result for discrete‐time neural network with time‐varying delay is given for the first time. Copyright © 2012 John Wiley & Sons, Ltd.     

Improved results on robust exponential stability criteria for neutral-type delayed neural networks

In this paper, we investigate the problem of robust global exponential stability analysis for a class of neutral-type neural networks. The interval time-varying delays allow for both slow and fast time-varying delays. The values of the time-varying uncertain parameters are assumed to be bounded within given compact sets. Improved global exponential stability condition is derived by employing new Lyapunov-Krasovskii functional and the integral inequality. The developed nominal and robust stability criteria is delay-dependent and characterized by linear-matrix inequalities (LMIs). The developed results are less conservative than previous published ones in the literature, which are illustrated by representative numerical examples.     

Robust stability of uncertain impulsive dynamical systems

This paper studies robust stability of uncertain impulsive dynamical systems. By introducing the concepts of uniformly positive definite matrix functions and Hamilton–Jacobi/Riccati inequalities, several criteria on robust stability, robust asymptotic stability and robust exponential stability are established. An example is also worked through to illustrate our results.     

Exponential stability criteria for discrete-time recurrent neural networks with time-varying delay

In this paper, the robust global exponential stability is investigated for the discrete-time recurrent neural networks (RNNs) with time-varying interval delay. By choosing an augmented Lyapunov–Krasovskii functional, delay-dependent results guaranteeing the global exponential stability and the robust exponential stability of the concerned neural network are obtained. The results are shown to be a generalization of some previous results, and less conservative than the existing works. Two numerical examples are given to demonstrate the applicability of the proposed method.     

Robust exponential stability of impulsive switched systems with switching delays: A Razumikhin approach

In this paper, we focus on the robust exponential stability of a class of uncertain nonlinear impulsive switched systems with switching delays. We introduce a novel type of piecewise Lyapunov-Razumikhin functions. Such functions can efficiently eliminate the impulsive and switching jump of adjacent Lyapunov functions at impulsive switching instants. By Razumikhin technique, the delay-independent criteria of exponential stability are established on the minimum dwell time. Finally, an illustrative numerical example is presented to show the effectiveness of the obtained theoretical results.     

Stability analysis of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms

In this paper, we investigate a class of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. By establishing an integro-differential inequality with impulsive initial conditions and applying M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness, global exponential stability and global robust exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. An example is given to illustrate the results obtained here.