不确定广义时滞系统的鲁棒稳定方法研究

研究一类带有时滞不确定广义系统的鲁棒渐近稳定问题．利用Lyapunov稳定性定理和线性矩阵不等式工具,得到了广义系统正则、鲁棒渐近稳定的时滞相关充分条件．为了降低所得结果的保守性,避免了采用系统模型变换和利用矩阵不等式方法．进一步,建立一个具有线性矩阵不等式约束的凸优化问题,利用Matlab软件中的LMI工具箱求解,得到保证广义系统鲁棒渐近稳定的最大可允许时滞上界．仿真示例表明了该方法的有效性．     

随机Markov跳跃时滞系统的鲁棒H∞指数滤波器设计

研究了一类不确定随机Markov跳跃时滞系统的鲁棒H∞指数滤波问题.应用Lyapunov-Krasovskii稳定性理论和广义Finsler引理,建立了滤波器存在的时滞相关条件,避免了使用模型变换方法和自由权矩阵方法所带来的保守性和繁冗的计算,鲁棒H∞指数滤波器可通过求解联立线性矩阵不等式设计.数值例子说明了所采用方法的有效性.     

执行器失效不确定时滞系统的指定衰减度鲁棒可靠控制

针对一类含有时变时滞的不确定参数线性系统,研究了在执行器发生故障情况下的鲁棒可靠控制器设计问题.系统中的参数不确定性满足广义匹配条件,时变时滞的大小及其变化率有界,并假设故障执行器元件的输出为零.经过适当的模型变换,将原系统的鲁棒指数镇定问题转化为另一个等价系统的鲁棒镇定问题.根据Lyapunov稳定性理论和线性矩阵不等式(LMI)方法,分别给出了鲁棒可靠控制器存在的时滞无关和时滞相关充分条件.仿真结果表明了该控制器设计方法的有效性.     

非线性模糊时滞系统鲁棒自适应控制

研究一类基于模糊T-S模型的非线性时滞系统鲁棒镇定问题．基于记忆型状态反馈策略，首先给出由T-S模糊模型描述的非线性时滞系统在时滞精确已知情况下的鲁棒镇定准则；然后给出非线性时滞系统在时滞未知情况下的鲁棒自适应控制策略．所设计的控制器可确保闭环系统渐近稳定，且具有良好的可操作性．最后通过仿真实例证明了该方法的正确性和有效性．     

传感器失效不确定时滞系统指数稳定可靠控制

针对一类含有时变时滞的不确定参数线性系统,研究了在传感器发生故障情况下系统指数稳定鲁棒可靠控制器设计问题。系统中的参数不确定性满足广义匹配条件,时变时滞及其变化率有界,并假设故障传感器元件的输出为零。经过适当的状态变换,将原系统的指数稳定鲁棒可靠控制问题转化为另一个等价系统的鲁棒可靠控制问题。根据Lyapunov稳定性理论,得到了系统存在指数稳定鲁棒可靠控制器应满足的一个矩阵不等式。为了便于数值求解,将该矩阵不等式转化为线性矩阵不等式（LMI）,并给出了可靠控制器的设计方法和步骤。利用该文方法设计的指数稳定鲁棒可靠控制器能够使得时滞系统对于任意允许的不确定性以及传感器失效都具有指定衰减度的渐近稳定性。数值算例说明了所提出设计方法的有效性。     

不确定多重状态时滞离散系统的LMI鲁棒稳定条件

研究了具有多重状态时滞的凸多面体不确定离散系统的鲁棒稳定性分析问题．基于参数依赖的李亚普诺夫稳定性和线性矩阵不等式推导出使得时滞鲁棒稳定系统鲁棒稳定的充分条件．应用此条件，通过测试一组线性矩阵不等式的可解性即可达到判定系统的鲁棒稳定性的目的．因为使用了参数依赖的李亚普诺夫稳定性思想，此鲁棒稳定条件比基于二次稳定概念的稳定条件的保守性更小．算例验证了结果．     

具有脉冲干扰和可变时滞的区间关联大系统的鲁棒指数稳定性

研究了一类具有脉冲干扰和可变时滞区间关联大系统的鲁棒指数稳定性.假设该系统的关联函数满足全局Lipschitz条件,基于矢量Lyapunov函数法和数学归纳法,给出确保该关联系统鲁棒指数稳定的充分条件.最后给出一个数值算例用以说明本文所得到结论的正确性和有效性.     

T-S 模糊时滞模型的时滞相关稳定性分析和镇定

研究一类用T—S模糊模型描述的非线性不确定时滞系统的时滞相关鲁棒镇定问题．基于线性矩阵不等式的可行解，首先给出利用T—S模糊模型描述的非线性时滞系统时滞相关稳定性准则；然后给出了经状态反馈鲁棒镇定设计的新方法．所设计的控制器能确保闭环系统渐近稳定．     

时滞切换不确定神经网络系统的指数稳定性

本文研究了具有无穷时滞切换不确定细胞神经网络(UCNNs)系统任意切换下的指数稳定性.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Lyapunov泛函方法,研究了时滞切换UCNNs系统任意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.     

线性定常时滞系统的鲁棒控制

在建立线性定常时滞系统模型的基础上,给出了时滞系统是独立鲁棒稳定的条件,并利用Lyapunov理论给予一些定理的证明,同时设计出了使闭环系统时滞独立鲁棒稳定的状态反馈控制器（即时滞独立鲁棒镇定问题）,最后给出的一个数值例子中用Matlab编程求出了符合条件的正定矩阵和控制律,从算例结果验证了所得出的结论。     

Robustly Exponential Stability Analysis for Discrete-Time Stochastic Neural Networks with Interval Time-Varying Delays

This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete-time stochastic neural networks with interval time-varying delay. By using the linear matrix inequality method and the free-weighting matrix technique, we construct a new Lyapunov–Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete-time stochastic neural networks with interval time-varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete-time stochastic neural networks. Based on the state feedback control law, some novel delay-dependent criteria of the robust exponential stabilization for a class of discrete-time stochastic neural networks with interval time-varying delay are established. The controller gains are designed to ensure the global robust exponential stability of the closed-loop systems. Finally, numerical examples illustrate the effectiveness of the theoretical results we have obtained.     

Robust Stability in Cohen–Grossberg Neural Network with both Time-Varying and Distributed Delays

In this article, the global exponential robust stability is investigated for Cohen–Grossberg neural network with both time-varying and distributed delays. The parameter uncertainties are assumed to be time-invariant and bounded, and belong to given compact sets. Applying the idea of vector Lyapunov function, M-matrix theory and analysis techniques, several sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential robust stability of the equilibrium point for the neural network. The methodology developed in this article is shown to be simple and effective for the exponential robust stability analysis of neural networks with time-varying delays and distributed delays. The results obtained in this article extend and improve a few recently known results and remove some restrictions on the neural networks. Three examples are given to show the usefulness of the obtained results that are less restrictive than recently known criteria.      

Relaxed delay-dependent exponential stability condition for a class of neural networks with polytopic uncertainties and distributed delays

The global robust exponential stability of a class of neural networks with polytopic uncertainties and distributed delays is investigated in this paper.Parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices are employed to obtain sufficient condition that guarantee the robust global exponential stability of the equilibrium point of the considered neural networks.The derived sufficient condition is proposed in terms of a set of relaxed linear matrix inequalities (LMIs),which can be checked easily by recently developed algorithms solving LMIs.A numerical example is given to demonstrate the effectiveness of the proposed criteria.     

Novel criteria for global robust exponential stability of neural networks with time-varying delays via LMI approach

The article addresses the problem of global robust exponential stability of interval neural networks with time-varying delays. On the basis of linear matrix inequality technique and M-matrix theory, some novel sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point for delayed interval neural networks are presented. It is shown that our results improve and generalize some previously published ones. Some numerical examples and simulations are given to show the effectiveness of the obtained results.     

Stochastic dissipativity analysis on discrete-time neural networks with time-varying delays

In this paper, the problems of global dissipativity and global exponential dissipativity are investigated for discrete-time stochastic neural networks with time-varying delays and general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing stochastic analysis technique, several new delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of the addressed neural networks are established in linear matrix inequalities (LMIs). Furthermore, when the parameter uncertainties appear in the discrete-time stochastic neural networks with time-varying delays, the delay-dependent robust dissipativity criteria are also presented. Two examples are given to show the effectiveness and less conservatism of the proposed criteria.     

Novel robust stability criteria of discrete-time stochastic recurrent neural networks with time delay

The problem of robust global exponential stability is investigated for a class of stochastic uncertain discrete-time recurrent neural networks with time delay. In this paper, the midpoint of the time delay's variation interval is introduced, and the variation interval is divided into two subintervals. Then, by constructing a new Lyapunov–Krasovskii functional and checking its variation in the two subintervals, respectively, some novel delay-dependent stability criteria for the addressed neural networks are derived. Numerical examples are provided to show that the achieved conditions are less conservative than some existing ones in the literature.     

Global exponential robust stability of reaction–diffusion interval neural networks with continuously distributed delays

This paper considers the existence of the equilibrium point and its global exponential robust stability for reaction-diffusion interval neural networks with variable coefficients and distributed delays by means of the topological degree theory and Lyapunov-functional method. The sufficient conditions on global exponential robust stability established in this paper are easily verifiable. An example is presented to demonstrate the effectiveness and efficiency of our results.     

Global Asymptotic Robust Stability and Global Exponential Robust Stability of Neural Networks with Time-Varying Delays

In this paper, based on nonnegative matrix theory, the Halanay’s inequality and Lyapunov functional, some novel sufficient conditions for global asymptotic robust stability and global exponential robust stability of neural networks with time-varying delays are presented. It is shown that our results improve and generalize several previous results derived in the literatures. From the obtained results, some linear matrix inequality criteria are derived. Finally, a simulation is given to show the effectiveness of the results.     

Robust Stability of Cohen–Grossberg Neural Networks via State Transmission Matrix

This brief is concerned with the global robust exponential stability of a class of interval Cohen-Grossberg neural networks with both multiple time-varying delays and continuously distributed delays. Some new sufficient robust stability conditions are established in the form of state transmission matrix, which are different from the existing ones. Furthermore, a sufficient condition is also established to guarantee the global stability for this class of Cohen-Grossberg neural networks without uncertainties. Three examples are used to show the effectiveness of the obtained results.     

Global Robust Exponential Stability of Interval General BAM Neural Network with Delays

In this paper, a class of interval general bidirectional associative memory (BAM) neural networks with delays are introduced and studied, which include many well-known neural networks as special cases. By using fixed point technic, we prove an existence and uniqueness of the equilibrium point for the interval general BAM neural networks with delays. By using a proper Lyapunov functions, we get a sufficient condition to ensure the global robust exponential stability for the interval general BAM neural networks with delays, and we just require that activation function is globally Lipschitz continuous, which is less conservative and less restrictive than the monotonic assumption in previous results. In the last section, we also give an example to demonstrate the validity of our stability result for interval neural networks with delays.