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Vector-Lyapunov-Function-Based Input-to-State Stability of Stochastic Impulsive Switched Time-Delay Systems

机译:基于向量-Lyapunov函数的随机脉冲切换时滞系统的输入至状态稳定性

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摘要

In this paper, the input-to-state stability is studied for stochastic impulsive switched time-delay systems. Using the vector Lyapunov function, average dwell time, and the properties of M-matrix, different types of sufficient conditions are established. Both the case that the continuous dynamics is stable and the case that the discrete dynamics is stable are addressed, and the stability conditions are obtained. In the obtained stability conditions, different components of the vector Lyapunov function are allowed to be coupled; the information in consecutive impulsive switching intervals is also allowed to be coupled. Therefore, the magnification on the corresponding coupling items is avoided and the obtained results are more general and less conservative than the existing results. Furthermore, we investigate the relationships among the vector Lyapunov function approach, the approach based on the comparison principle and the scalar Lyapunov function approach. According to the vector Lyapunov function, the comparison system is constructed and the scalar-Lyapunov-function-based stability conditions are established. Finally, the applicability of our results is illustrated through two examples from neural systems and the synchronization problem of chaos-based secure communication systems.
机译:本文研究了随机脉冲切换时滞系统的输入状态稳定性。使用向量Lyapunov函数,平均停留时间和M矩阵的属性,建立了不同类型的充分条件。讨论了连续动力学稳定的情况和离散动力学稳定的情况,并获得了稳定条件。在所获得的稳定性条件下,允许向量Lyapunov函数的不同分量耦合。在连续的脉冲切换间隔中的信息也被允许耦合。因此,避免了在相应的耦合项上的放大,并且所获得的结果比现有结果更普遍,更不保守。此外,我们研究了向量李雅普诺夫函数方法,基于比较原理的方法和标量李雅普诺夫函数方法之间的关系。根据向量李雅普诺夫函数,构建了比较系统,建立了基于标量-李雅普诺夫函数的稳定性条件。最后,通过神经系统的两个例子和基于混沌的安全通信系统的同步问题,说明了我们的结果的适用性。

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