多时滞退化微分系统指数稳定的代数判据

本文研究退化多时滞微分系统的指数稳定性,得到了退化多时滞微分系统指数稳定性的代数判据,给出的一个例子说明所得结果的应用,同时给出了中立型退化时滞微分系统指数稳定的判定方法.     

退化时滞中立型微分系统的特征根分布与指数稳定

本文首先讨论了两类具有一般意义的线性自治中立型时滞微分系统特征根链的分布情况,并且给出特征根链的逼近链拟合曲线,然后重点讨论多时滞退化中立型微分系统的特征根链分布,由此得到多时滞退化中立型微分系统指数稳定的条件.     

一类复杂时滞系统的λ实用稳定性

主要研究了一类具有多时滞的复杂微分系统的λ实用稳定性问题,通过对Lyapunov函数方法以及比较原理的运用,建立一般形式的实用稳定性直接判据.在此基础上给出讨论这类多时滞复杂微分系统实用稳定性的新方法.     

结构振动主动控制的多时滞控制律的设计方法

对遭受地震激励的建筑结构主动控制中的多时滞问题进行研究.通过一种特殊的系统状态变量增广,将包含有多时滞项的系统状态方程转化为形式上不包含时滞的标准离散形式,然后采用离散最优控制方法设计时滞控制律.最后通过数值仿真验证了多时滞控制律的有效性.仿真结果显示,若对时滞不进行处理,控制系统会在很小时滞量的情况下出现发散,而该文中的时滞问题处理方法能够取得良好的控制效果,而且该方法不但能处理小时滞量问题,也能处理大时滞量问题.     

变时滞的退化滞后型微分系统的稳定性

主要研究了具有变时滞的退化时滞微分系统的稳定性.利用退化时滞微分系统的变易公式和Gronwall-Bellman积分不等式给出了该系统的指数估计以及稳定和指数渐近稳定的充分条件.     

多时滞线性切换系统的稳定性及其反馈镇定

研究了具有多时滞线性切换系统的稳定性及其反馈镇定问题,利用完备性条件、矩阵分解与二次Lyapunov泛函,给出了多时滞切换系统渐近稳定的充分条件和切换律设计方法.在此基础上,研究了这类系统的镇定控制问题,设计了保证系统时滞独立渐近镇定的控制器.     

退化时滞微分系统的特征根分布与指数稳定

该文首先研究了退化时滞微分系统的特征根分布, 指出如果退化时滞微分系统的所有特征根都具有负实部, 在一个条件下, 特征根的负实部的最大值为负.由此可以得到一个条件, 在该条件下如果所有特征根都具有负实部, 则退化时滞微分系统的解是指数稳定的.作为例子, 对中立型给出其解为指数稳定的条件.     

基于输出反馈的一类时滞系统鲁棒H∞容错控制

针对状态具有多个时滞的线性不确定性系统,基于李雅普诺夫稳定性理论,矩阵不等式及线性矩阵不等式方法,设计带有多个时滞记忆的输出反馈鲁棒H∞容错控制器.研究了在执行器发生故障的情况下,多时滞不确定性系统的鲁棒H∞容错控制的问题.首先给出了多个时滞记忆的输出反馈鲁棒H∞容错控制器的综合分析,进一步给出参数不确定项的多时滞系统的渐近稳定的充分条件,以及满足给定性能指标鲁棒H∞容错控制器的设计方法.仿真结果证明了该方法的有效性和可行性.     

具有时变时滞的非线性分数阶退化微分系统的有限时间稳定性

本文研究了一类具有时变时滞的非线性分数阶退化微分系统的有限时间稳定性问题.首先通过退化微分系统理论得到了系统正则无脉冲的充分性条件.在此基础上通过建立Lyapunov泛函,并利用广义Gronwall不等式和线性矩阵不等式方法给出了含有时变时滞因素的分数阶退化微分系统的有限时间稳定性判据.最后给出具体的算例验证了定理条件的有效性.     

非线性随机时滞微分系统的脉冲镇定[英文]

本文研究一类非线性随机时滞微分系统的脉冲镇定.利用Lyapunov函数,Razumikhin和一些分析的技巧得到系统基于线性矩阵不等式形式的均方稳定性判据,该判据表明适当的脉冲可以用来镇定不稳定的随机时滞系统.与此同时,数值例子及仿真证明了本文方法的有效性.     

Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying delays

This paper investigates delay-dependent robust exponential state estimation of Markovian jumping fuzzy neural networks with mixed random time-varying delay. In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the robust exponential state estimation of Markovian jumping Hopfield neural networks with mixed random time-varying delays. Moreover probabilistic delay satisfies a certain probability-distribution. By introducing a stochastic variable with a Bernoulli distribution, the neural networks with random time delays is transformed into one with deterministic delays and stochastic parameters. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delays, the dynamics of the estimation error is globally exponentially stable in the mean square. Based on the Lyapunov–Krasovskii functional and stochastic analysis approach, several delay-dependent robust state estimators for such T–S fuzzy Markovian jumping Hopfield neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving a delay-dependent LMI. Finally some numerical examples are provided to demonstrate the effectiveness of the proposed method.     

Delay Range-Dependent Stability Analysis for Markovian Jumping Stochastic Systems with Nonlinear Perturbations

This article addresses the problem of delay-dependent stability for Markovian jumping stochastic systems with interval time-varying delays and nonlinear perturbations. The delay is assumed to be time-varying and belongs to a given interval. By resorting to Lyapunov–Krasovskii functionals and stochastic stability theory, a new delay interval-dependent stability criterion for the system is obtained. It is shown that the addressed problem can be solved if a set of linear matrix inequalities (LMIs) are feasible. Finally, a numerical example is employed to illustrate the effectiveness and less conservativeness of the developed techniques.     

Delay-dependent stochastic stability of delayed Hopfield neural networks with Markovian jump parameters

In this paper, the problem of stochastic stability for a class of time-delay Hopfield neural networks with Markovian jump parameters is investigated. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. Without assuming the boundedness, monotonicity and differentiability of the activation functions, some results for delay-dependent stochastic stability criteria for the Markovian jumping Hopfield neural networks (MJDHNNs) with time-delay are developed. We establish that the sufficient conditions can be essentially solved in terms of linear matrix inequalities.     

Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time‐varying delays

This article discusses the issue of robust stability analysis for a class of Markovian jumping stochastic neural networks (NNs) with probabilistic time‐varying delays. The jumping parameters are represented as a continuous‐time discrete‐state Markov chain. Using the stochastic stability theory, properties of Brownian motion, the information of probabilistic time‐varying delay, the generalized Ito's formula, and linear matrix inequality (LMI) technique, some novel sufficient conditions are obtained to guarantee the stochastical stability of the given NNs. In particular, the activation functions considered in this article are reasonably general in view of the fact that they may depend on Markovian jump parameters and they are more general than those usual Lipschitz conditions. The main features of this article are described in the following: first one is that, based on generalized Finsler lemma, some improved delay‐dependent stability criteria are established and the second one is that the nonlinear stochastic perturbation acting on the system satisfies a class of Lipschitz linear growth conditions. By resorting to the Lyapunov–Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established using an efficient LMI approach. Finally, two numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results. © 2014 Wiley Periodicals, Inc. Complexity 21: 59–72, 2016     

State estimation for Markovian jumping genetic regulatory networks with random delays

In this paper, the state estimation problem is investigated for stochastic genetic regulatory networks (GRNs) with random delays and Markovian jumping parameters. The delay considered is assumed to be satisfying a certain stochastic characteristic. Meantime, the delays of GRNs are described by a binary switching sequence satisfying a conditional probability distribution. The aim of this paper is to design a state estimator to estimate the true states of the considered GRNs through the available output measurements. By using Lyapunov functional and some stochastic analysis techniques, the stability criteria of the estimation error systems are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable. Then, the explicit expression of the desired estimator is shown. Finally, a numerical example is presented to show the effectiveness of the proposed results.     

Robust stability criteria for uncertain neutral type stochastic system with Takagi–Sugeno fuzzy model and Markovian jumping parameters

In this paper, the robust stability for uncertain neutral stochastic system with Takagi–Sugeno (T–S) fuzzy model and Markovian jumping parameters (MJPs) are investigated. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite-state space. Some novel sufficient conditions are derived to guarantee the asymptotic stability of the equilibrium point in the mean square. By utilizing the Lyapunov–Krasovskii functional, stochastic analysis theory, some free weighting matrices and linear matrix inequality (LMI) technique, the upper bound of time-varying delay is obtained by using Matlab® control toolbox. Finally, some numerical examples are given to show the effectiveness of the obtained results.     

STOCHASTIC STABILITY OF UNCERTAIN RECURRENT NEURAL NETWORKS WITH MARKOVIAN JUMPING PARAMETERS

In this paper, global robust stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters is considered. A novel Linear matrix inequality(LMI) based stability criterion is obtained to guarantee the asymptotic stability of uncertain stochastic recurrent neural networks with Markovian jumping parameters.The results are derived by using the Lyapunov functional technique, Lipchitz condition and S-procuture. Finally, numerical examples are given to demonstrate the correctness of the theoretical results. Our results are also compared with results discussed in [31] and [34]to show the effectiveness and conservativeness.     

Sliding mode ℋ∞ control design for uncertain nonlinear stochastic state-delayed Markovian jump systems with actuator failures

This paper presents a new approach for sliding mode control (SMC) design of a class of uncertain nonlinear stochastic Markovian jump systems (MJSs) with time-varying delay. The attention is focused on removing a structural assumption, under which several studies concerning SMC to accommodate Itô stochastic systems have been reported. Sufficient conditions in terms of LMIs are established to guarantee the existence of the sliding surface, then the reachability is analyzed. Compared with the existing results, the mode-independent sliding surface can be derived to weaken jumping effect. The theoretical results are illustrated by a numerical example.     

New delay‐dependent global robust passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays

The problem of passivity analysis for stochastic neural networks with Markovian jumping parameters and interval time‐varying delays is investigated in this article. By constructing a novel Lyapunov–Krasovskii functional based on the complete delay‐decomposing idea and using improved free‐weighting matrix method, some improved delay‐dependent passivity criteria are established in terms of linear matrix inequalities. Numerical examples are also given to show the effectiveness of the proposed methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 167–179, 2016     

H∞-Control for Linear Time-Delay Systems with Markovian Jumping Parameters

This paper deals with the robust H-control problem for uncertain continuous-time linear time-delay systems with Markovian jumping parameters. Based on Lyapunov functional approach, a sufficient condition that assures the robust stochastic stabilizability and preserves the H-disturbance attenuation for the uncertain class of linear time-delay systems with Markovian jumping parameters is established. The uncertainties considered in this paper are of the norm-bounded type. A numerical example is given to demonstrate the usefulness of the results.