一类区间时变时滞系统的稳定性分析

针对一类区间时变时滞系统的稳定性问题,进行了全局渐近稳定性分析.通过引入时滞分段方法和构建恰当的Lyapunov-Krasovskii泛函,得到了新的区间时滞相关稳定性判定准则.该准则以线性矩阵不等式形式给出,便于利用LMI工具箱对系统的稳定性进行判定.新准则具有较少的保守性,并且在一定范围内保守性随着时滞分段增多而减少,即时滞分段越多,保守性越少.数值仿真算结果例表明了新准则所具有的有效性和较少的保守性.     

一种改进的区间时变时滞系统稳定性准则北大核心CSCD

研究区间时变时滞系统的稳定性问题。首先,通过引入一个区间调节参数,将系统时滞区间分解为两部分之和。其次,在两个子区间的基础上构造一个包含更多时滞信息和三重积分项的新Lyapunov-Krasovskii (L-K)泛函。在处理来自L-K泛函求导过程中产生的积分交叉项时,将时滞分属于各子区间的情况作为一个整体来研究。然后,基于Lyapunov稳定性理论,以线性矩阵不等式的形式给出一个新的稳定性准则。最后,通过两个数例证明所得稳定性准则的有效性。     

带有非线性扰动的时变时滞系统的稳定性准则

研究了带有非线性扰动的时变时滞系统的稳定性问题.基于时滞分割方法,提出了保守性更小的系统稳定性分析准则.利用一个自由参数将时滞区间分割为2个子区间,进而构造了含有时滞分割点状态信息和3重积分项的Lyapunov-Krasovskii泛函,并采用自由矩阵积分不等式界定泛函导数中的交叉项.基于Lyapunov稳定性定理,得到了以线性矩阵不等式描述的时滞相关型稳定性准则.数值算例表明该稳定性准则能够得到更大的时滞上界,与已有结果相比具有更小的保守性.     

不确定线性系统新的稳定性准则

研究了带有区间时滞的不确定系统的稳定性问题。通过采用时滞分割法,把时滞区间分割成任意两小段,并构造恰当的Lyapunov函数,利用积分不等式,得到了新的时滞相关的稳定性准则。通过数值例子验证了结果的有效性。     

T-S模糊时滞系统的时滞相关镇定

基于T-S模糊模型方法,研究一类非线性区间时滞系统的稳定性分析与镇定问题,基于一组线性矩阵不等式的可解性,给出了非线性时滞系统时滞相关稳定和镇定的充分条件,并通过构造新的模糊权依赖型Lyapunov泛函,降低了稳定性准则的保守性,最后通过仿真例子表明了所提方法的有效性.     

一类新的Takagi-Sugeno模糊时滞系统的稳定性准则

本文提出一种新的时滞划分方法—变时滞划分法,以解决连续延时Takagi-Sugeno模糊系统的稳定性和镇定性问题.不同于已有的文献,用可变参数将时变时滞区间[0,d(t)]划分为若干个可变子区间,并得出模糊时滞系统的新的时滞相关稳定性准则.本文提出的新方法能充分利用时滞子区间的内部信息,因此新的时滞相关稳定性准则比以往结果具有更小的保守性.基于Lyapunov稳定性理论,以线性矩阵不等式形式给出T--S模糊系统的新的时滞相关稳定性准则,并将稳定性和镇定性研究结果扩展到具有不确定参数的T--S模糊系统.仿真实例证明了本文方法降低保守性的有效性.     

不确定切换奇异时滞系统鲁棒指数容许性分析

讨论一类连续时间不确定切换奇异区间时变时滞系统的鲁棒指数容许性问题. 通过定义衰减率依赖李亚普诺夫函数并利用平均驻留时间法, 给出一个时滞区间依赖充分条件保证标称系统正则、无脉冲且均方指数稳定. 同时该准则也被推广至不确定系统. 本文获得的结论为连续时间切换奇异时滞系统的基本问题提供了一个解, 即识别切换信号使得切换奇异时滞系统正则、无脉冲且均方指数稳定. 数值例子说明本文结果的有效性.     

一类区间时变时滞T-S模糊系统的鲁棒控制

针对一类区同时变时滞T-S模糊系统,研究了其时滞相关渐近稳定性以及控制器设计问题.基于Lyapunov稳定性理论和线性矩阵不等式(LMI)工具,并结合自由权矩阵方法,设计一个包含时滞区间均值在内的新Lyapunov-Krasovskii泛函,给出了改进的时滞T-S模糊系统渐近稳定的时滞相关准则.同时,根据并行分布补偿算法,给出了带有记忆的状态反馈模糊控制器的设计方法.最后,实例仿真表明了方法的有效性.     

含区间时变时滞的线性不确定系统非脆弱鲁棒控制

针对一类时滞是时变的且属于一个已知区间的线性系统,利用Lyapunov-Krasovskii泛函方法,结合积分不等式方法,以线性矩阵不等式的形式,给出了区间时滞线性系统的时滞相关鲁棒稳定性准则,并设计了非脆弱鲁棒控制器。数值算例表明,所得结论是有效的,具有更低的保守性。     

T-S模糊时滞系统的时滞相关镇定

基于T-S模糊模型方法,研究一类非线性区间时滞系统的稳定性分析与镇定问题.基于一组线性矩阵不等式的可解性,给出了非线性时滞系统时滞相关稳定和镇定的充分条件,并通过构造新的模糊权依赖型Lyapunov泛函,降低了稳定性准则的保守性.最后通过仿真例子表明了所提方法的有效性.

     

Passivity and Pinning Passivity of Coupled Delayed Reaction–Diffusion Neural Networks with Dirichlet Boundary Conditions

This paper respectively considers passivity problem and pinning passivity problem for coupled delayed reaction–diffusion neural networks (CDRDNNs). By construction of appropriate Lyapunov functionals and utilization of inequality techniques, several passivity conditions are derived for the CDRDNNs. Moreover, the pinning control technique is developed to obtain some passivity criteria for CDRDNNs. Finally, two numerical examples are also provided to verify the correctness of the theoretical results.     

Passivity and robust synchronisation of switched interval coupled neural networks with time delay

This paper is concerned with passivity and robust synchronisation of switched coupled neural networks with uncertain parameters. First, the mathematical model of switched coupled neural networks with interval uncertain parameters is established, which consists of L modes and switches from one mode to another according to the switching rule. Second, by employing passivity theory and linear matrix inequality techniques, delay-independent and delay-dependent conditions are derived to guarantee the passivity of switched interval coupled neural networks. Moreover, based on the proposed passivity results, global synchronisation criteria can be obtained for switched coupled neural networks with or without uncertain parameters. Finally, an illustrative example is provided to demonstrate the effectiveness of the obtained results.     

Passivity analysis for uncertain discrete-time stochastic BAM neural networks with time-varying delays

This paper is concerned with the passivity analysis problem for a class of discrete-time stochastic bidirectional associative memory neural networks with time-varying delays. Furthermore, the results are extended to the robust passivity analysis with mixed time delays that consist of both the discrete and distributed time delays, and the uncertainties are assumed to be time-varying norm bounded parameter uncertainties. By constructing a new Lyapunov–Krasovskii functional and introducing some appropriate free-weighting matrices, a delay-dependent passivity criterion is derived in terms of LMIs whose feasibility can be easily checked by some available software packages. Finally, two numerical examples with simulation results are given to demonstrate the effectiveness and usefulness of the proposed results.     

Passivity analysis of stochastic delayed neural networks with Markovian switching

In this paper, the problem of passivity analysis is investigated for a class of stochastic delayed neural networks with Markovian switching. By applying Lyapunov functional and free-weighting matrix, delay-dependent/independent passivity criteria are presented in terms of linear matrix inequalities. The results herein include existing ones for neural networks without Markovian switching as special cases. An example is given to demonstrate the effectiveness of the proposed criteria.     

Pinning exponential synchronisation and passivity of coupled delayed reaction–diffusion neural networks with and without parametric uncertainties

In this paper, we focus on the pinning exponential synchronisation and passivity of coupled reaction–diffusion neural networks (CRDNNs) with and without parametric uncertainties, respectively. On the one hand, with the help of designed nonlinear pinning controllers and Lyapunov functional method, sufficient conditions are established to let the CRDNNs with hybrid coupling and mixed time-varying delays realise exponential synchronisation and passivity. On the other hand, considering that the external perturbations may lead the reaction–diffusion neural networks (RDNNs) parameters to containing uncertainties, the robust pinning exponential synchronisation and robust pinning passivity for coupled delayed RDNNs with parametric uncertainties are investigated by designing appropriate pinning control strategies. Finally, the effectiveness of the theoretical results are substantiated by the two given numerical examples.     

Finite-time robust passive control for a class of switched reaction-diffusion stochastic complex dynamical networks with coupling delays and impulsive control

Finite-time boundedness and finite-time passivity for a class of switched stochastic complex dynamical networks (CDNs) with coupling delays, parameter uncertainties, reaction-diffusion term and impulsive control are studied. Novel finite-time synchronisation criteria are derived based on passivity theory. This paper proposes a CDN consisting of N linearly and diffusively coupled identical reaction- diffusion neural networks. By constructing of a suitable Lyapunov–Krasovskii's functional and utilisation of Jensen’s inequality and Wirtinger's inequality, new finite-time passivity criteria for the networks are established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, two interesting numerical examples are given to show the effectiveness of the theoretical results.     

时变时滞不确定性神经网络的被动性准则

研究具有时变时滞不确定性神经网络的被动性问题。通过构造适当的Lyapunov泛函并利用一些分析技巧,给出一个新的条件,以确保与时变延迟的不确定性神经网络的被动性。被动条件以线性矩阵不等式(LMI)表示,可以很容易地通过有效内点算法进行求解。通过一个数例证明了该方法的有效性。     

Further Results on Dissipativity Criterion for Markovian Jump Discrete-Time Neural Networks with Two Delay Components Via Discrete Wirtinger Inequality Approach

This paper is concerned with strict \((\mathcal {Q}, \mathcal {S}, \mathcal {R})-\gamma \) - dissipativity and passivity analysis for discrete-time Markovian jump neural networks involving both leakage and discrete delays expressed in terms of two additive time-varying delay components. The discretized Wirtinger inequality is utilized to bound the forward difference of finite-sum term in the Lyapunov functional. By constructing a suitable Lyapunov–Krasovskii functional, sufficient conditions are derived to guarantee the dissipativity and passivity criteria of the proposed neural networks. These conditions are presented in terms of linear matrix inequalities (LMIs), which can be efficiently solved via LMI MATLAB Toolbox. Finally, numerical examples are given to illustrate the effectiveness of the proposed results.     

New exponential passivity of BAM neural networks with time-varying delays

In this paper, the exponential passivity for bidirectional associative memory (BAM) neural networks with time-varying delays is considered. In our study, the lower and upper bounds of the activation functions are allowed to be either positive, negative or zero. By constructing new and improved Lyapunov–Krasovskii functional and introducing free-weighting matrices, a new and improved delay-dependent exponential passivity criterion for BAM neural networks with time-varying delays is derived in the form of linear matrix inequality (LMI). A numerical example is given to show that the derived condition is less conservative than some existing results given in the literature.

     

Novel passivity criteria for neural networks with discrete and unbounded distributed time-varying delays

Novel passivity criteria are presented for the passivity of a class of cellular neural networks with discrete and unbounded distributed time-varying delays. Two types of uncertainty are considered: one is time-varying structured uncertainty while the other is interval uncertainty. The Gu's discretized Lyapunov–Krasovskii functional method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz–Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for passivity of delayed neural networks. A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing passivity criteria.