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基于状态时滞的 T-S 模糊系统的稳定性的改进模糊采样数据控制。

An Improved Fuzzy Sampled-Data Control to Stabilization of T-S Fuzzy Systems With State Delays.

出版信息

IEEE Trans Cybern. 2020 Jul;50(7):3125-3135. doi: 10.1109/TCYB.2019.2910520. Epub 2019 Apr 22.

Abstract

This paper deals with the issue of sampled-data stabilization for T-S fuzzy systems (TSFSs) with state delays and nonuniform sampling. First, a fuzzy membership function (FMFs)-dependent approach is proposed, which uses information not only on both the delayed state and actual sampling pattern but also on the FMFs. Second, the inner sampling interval is split into flexible terminals, and a novel FMFs-dependent Lyapunov-Krasovskii functional (LKF) is constructed. Meanwhile, a fuzzy sampled-data controller (FSDC) with a switched topology is designed to solve the tricky issue on the estimation of FMFs-dependent terms. Then, based on the LKF methodology, the extended Wirtinger's inequality, and an improved reciprocally convex combination strategy, some relaxed criteria with both a larger sampling period and upper bound of time delays for achieving the stabilization of TSFSs are derived. Two numerical examples are presented to demonstrate the superiority and applicability of the proposed scheme.

摘要

本文针对具有状态时滞和非均匀采样的 T-S 模糊系统(TSFSs)的采样数据稳定化问题进行了研究。首先,提出了一种基于模糊隶属度函数(FMFs)的方法,该方法不仅利用了时滞状态和实际采样模式的信息,还利用了 FMFs 的信息。其次,将内采样间隔划分为灵活的终端,并构建了一个新的基于 FMFs 的李雅普诺夫-克拉索夫斯基函数(LKF)。同时,设计了一个具有切换拓扑的模糊采样数据控制器(FSDC),以解决 FMFs 相关项估计的棘手问题。然后,基于 LKF 方法、扩展的 Wirtinger 不等式和改进的互凸组合策略,得出了一些具有更大采样周期和时滞上界的松弛准则,以实现 TSFSs 的稳定化。通过两个数值示例验证了所提出方案的优越性和适用性。

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