具有不确定转移概率的马尔科夫复杂网络的聚类同步

研究具有不确定转移概率的马尔科夫复杂网络系统的聚类同步问题,系统模型包含耦合的离散时变时滞和耦合的分布时变时滞.通过充分考虑转移概率的性质和不确定区域的特性,用一个含有较少变量的有效技术代替传统的Young不等式来约束转移率中的不确定项.同时,利用增广李雅普诺夫泛函和具有较小保守性的积分不等式,给出新的依赖时滞和时滞导数上下界的聚类同步准则.最后通过数值仿真验证所提出方法的有效性.     

基于仿射Bessel-Legendre不等式和不确定转移率的神经网络稳定性

针对具有时变时滞和不确定转移率的马尔科夫神经网络系统,充分考虑马尔科夫转移率的不确定特性,利用基于松弛变量的有效技术代替传统不等式来约束转移速率中的不确定项,从而减少了决策变量的个数并降低了计算复杂度.通过建立时滞依赖的增广Lyapunov-Krasovskii泛函,并基于仿射Bessel-Legendre(B-L)不...     

具有时变时滞的Lurie系统时滞依赖绝对稳定性新判据

考虑了一类具有时变时滞和范数有界不确定的Lurie控制系统绝对稳定性分析问题. 通过选取一个新的Lyapunov-Krasovskii泛函将整个时滞区间分为两段, 每段区间上定义了不同的能量函数. 并给出了由LMI描述的新的时滞依赖鲁棒绝对稳定性判据.     

含区间时变时滞的线性不确定系统鲁棒稳定性新判据

研究一类区间时变时滞线性不确定系统的鲁棒稳定性问题.通过引入增广Lyapunov泛函,结合积分不等式方法,导出了区间时变时滞线性系统的时滞相关鲁棒稳定性新判据.与现有方法不同,该方法不涉及自由权矩阵技术和任何模型变换,减少了理论和计算上的复杂性,而且在估计Lyapunov泛函导数的上界时没有忽略某些有用项.数值算例表明,所提出的判据是有效的,具有更低的保守性.     

一类具有区间时变多状态时滞系统的稳定性分析

研究一类具有区间时变多状态时滞且时滞导数不确定的系统的稳定性问题. 通过选择合理的Lyapunov-Krasovskii函数结合辅助变量和广义状态法, 以LMI的形式给出了时滞相关的稳定性充分条件. 文中的结论对时滞的导数没有任何限制, 可用于具有快时变时滞系统. 与已有的相关研究成果相比, 结论更具有一般性, 保守性也更低. 最后通过仿真及数值算例说明了本文方法的有效性和优越性.     

多性能指标下区间二型T-S模糊时滞系统的滤波器设计

区间二型T-S模糊时变延迟模型可以有效地处理具有参数不确定的非线性时变时滞系统.针对多性能指标框架下 区间二型T-S模糊时变延迟系统的滤波器设计仍然是有待解决的问题,利用Lyapunov-Krasovskii泛函方法,在多性能指标框架下提出前提隶属函数不匹配的区间二型模糊滤波器.利用最新发展的积分不等式,所设计的滤波误差系统可以同时满足耗散性、无源性、${H_\infty     

改进的时变时滞不确定离散系统的鲁棒稳定性分析

研究了一类具有区间时变时滞的不确定离散时间系统的鲁棒稳定性问题.首先,将时滞区间分成两个子区间,在不同的子区间上通过构造不同的Lyapunov泛函,再结合一个更紧的不等式,在没有忽略任何有用项的前提下,得到了基于线性矩阵不等式形式的时滞相关的稳定性判据.证明过程既没有利用模型变换也没有引进自由权矩阵,降低了结论的保守性和计算量.数值算例说明了该方法的有效性.     

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.     

基于推广的概率分布区间分解法的时滞系统稳定性分析

基于推广的概率分布区间分解法,研究一类具有随机时滞系统的概率分布相关稳定性问题.充分利用随机时滞的概率分布信息,获得一系列稳定性判据;通过严格的数学证明,表明通过增加概率区间数可以逐渐降低稳定性判据的保守性,从而建立一组新的分层结构LMI条件;严格证明了在采用相同概率区间划分的条件下,所得到的稳定性判据的保守性低于不考虑时滞概率分布的时变时滞分解法所得到的结果,并且分析和比较了两种方法的计算量.     

基于划分时滞区间的一类Cohen-Grossberg神经网络的鲁棒稳定性

研究一类具有时变时滞及参数不确性的Cohen-Grossberg神经网络的鲁棒稳定性问题.应用划分时滞区间的思想构造了一个新的Lyapunov泛函,并以线性矩阵不等式的形式给出了平衡点全局鲁棒稳定性判据,新判据放松了时变时滞变化率必须小于1的限制.仿真结果进一步证明了所得结论的有效性.     

Simultaneous Fault Detection and Control for Markovian Jump Systems with General Uncertain Transition Rates

This paper investigates the simultaneous control and fault detection of Markovian jump systems with general transition rates allowed to be unknown and known with uncertainties. By introducing slack matrices, a new approach is developed to conquer the nonlinearity induced by unknown and uncertain transition rates. Then, sufficient conditions are presented to ensure the stochastic stability of the resultant closed-loop system and meet the robust and detection performance indices. Finally, an example is given to illustrate the effectiveness of the proposed theoretical results.     

Passivity and passification for stochastic systems with Markovian switching and generally uncertain transition rates

The paper deals with the problems of passivity and passification for stochastic systems with Markovian switching and generally uncertain transition rates. The considered systems are more general, which cover uncertain transition rates and partly known transition rates as two special cases. By employing the multiple Lyapunov function and some free-weighting matrices, a state feedback controller is constructed such that the resulted closed-loop system is stochastically passive. Some sufficient conditions for the solution to the problem are derived in the form of linear matrix inequalities (LMIs). Finally, a numerical example is given to demonstrate the validity of the main results.

     

Sliding mode control for Markov jump linear uncertain systems with partly unknown transition rates

In this paper, based on sliding mode control approach, the robust stabilisation problem for a class of continuous-time Markovian jump linear uncertain systems with partly unknown transition rates is investigated. The transition rate matrix under consideration covers completely known, boundary known and completely unknown elements. By making use of linear matrix inequalities technique, sufficient conditions are presented to derive the linear switching surface and guarantee the stochastic stability of sliding mode dynamics. Then a sliding mode control law is designed to drive the state trajectory of the closed-loop system to the specified linear switching surface in finite time in spite of the existing uncertainties and unknown transition rates. Finally, an example is given to verify the validity of the theoretical results.     

An LMI Approach to consensus in second-order multi-agent systems

This paper deals with the consensus problem of the second-order multi-agent systems based on linear system theory. By a system transformation, the consensus problem is converted to the stability problem of a linear system. The second-order systems considered include the systems with both the fixed topology and the Markov switching topology. Necessary and sufficient conditions of consensus or mean square consensus are obtained. The results are extended to the cases of the uncertain transition probability rates and time-delay, respectively. Finally, a simulation example is given to show the effectiveness of the presented results.     

Nonfragile observer‐based H ∞ sliding mode control for Itô stochastic systems with Markovian switching

The paper is devoted to investigating sliding mode control for a class of nonlinear uncertain stochastic systems with input nonlinearity and Markovian switching. A nonfragile observer subjected to the transition rates of the modes is designed. By some specified matrices, the connections among the designed sliding surfaces corresponding to every mode are established. The state estimation‐based sliding mode control law is derived to guarantee the reachability of the sliding surface in finite time interval. The sufficient conditions on asymptotically stochastic stability of the error system and sliding mode dynamics with a given disturbance attenuation level are derived in terms of linear matrix inequalities. Finally, an example is provided to illustrate the efficiency of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust stability of Markovian jump discrete-time neural networks with partly unknown transition probabilities and mixed mode-dependent delays

This article discusses the robust stability problem for a class of uncertain Markovian jump discrete-time neural networks with partly unknown transition probabilities and mixed mode-dependent time delays. The transition probabilities of the mode jumps are considered to be partly unknown, which relax the traditional assumption in Markovian jump systems that all of them must be completely known a priori. The mixed time delays consist of both discrete and distributed delays that are dependent on the Markovian jump modes. By employing the Lyapunov functional and linear matrix inequality approach, some sufficient criteria are derived for the robust stability of the underlying systems. A numerical example is exploited to illustrate the developed theory.     

Robust H infinity suboptimal and guaranteed cost state feedbacks as solutions to linear-quadratic dynamic games under uncertainty

For linear-quadratic dynamic games in which norm-bounded time-varying uncertainty enters in all matrices of the system equation without assuming the matching conditions, the notions of robust minimax and maximin strategies are introduced. It is shown how to construct two auxiliary dynamic games with completely known equations in such a way that a minimax strategy for one of them and a maximin strategy for the other turn out to be the robust minimax and maximin strategies, respectively, for the original dynamic game. By assuming 'control' as the first player and 'disturbance' as the second player, we show that the robust minimax strategy in the appropriate dynamic game provides the uncertain system with a robust H infinity suboptimal control, which guarantees quadratic stability of the unforced uncertain system. The results presented in this paper generalize those obtained in previous works.     

具有长时延和丢包的网络控制系统稳定性分析

针对同时出现长网络诱导时延和丢包的网络控制系统,研究了带不确定性的网络控制系统的稳定性问题。基于一定的数据包丢失率,系统被建模成带结构事件率约束的异步动态系统。利用李亚普诺夫方法和线性矩阵不等式相关知识,结合异步动态系统的稳定性理论,推出了使不确定网络控制系统指数稳定的充分条件,并给出了保证系统指数稳定的数据传输成功率满足范围;最后利用MATLAB仿真的数值例子验证了此方法的有效性和可行性。