Stability and stabilization of network T-S fuzzy systems with random packet-loss for Synchronous machine

IF 1.9 4区 数学 Q1 MATHEMATICS Iranian Journal of Fuzzy Systems Pub Date : 2021-02-01 DOI:10.22111/IJFS.2021.5870
M. S. Aslam, X. Dai, T. Zhao
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Abstract

This article deals with the problem H∞ control under network T-S fuzzy system with random packet dropouts for the synchronous machine. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. Two main approaches adopted in this paper to design the fuzzy controller. First, a new version of the T-S fuzzy state-feedback controller is constructed to guarantee the resulting closed-loop system to be stochastically stable. Furthermore, in this regard, by taking into account a new version of fuzzy Lyapunov-Krasovskii functional (LKF) in conjunction with free weighting matrices, containing mode-dependent non-integral term such that the resulting system is stochastically stable with the desired performance. Second, to establish the pole place method (PPM) with the membership function. Furthermore, to make the controller design more convenient, the designed controller does not need to share the number of fuzzy rules and membership functions with the model. Synchronous machine parameters are used for the simulation results which are provided to show the usefulness of the proposed algorithm.
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同步机随机丢包网络T-S模糊系统的稳定性与稳定化
研究了同步机随机丢包网络T-S模糊系统下的H∞控制问题。在控制设计中,考虑了闭环系统中存在的间歇性数据丢失问题。本文主要采用两种方法来设计模糊控制器。首先,构造了一种新的T-S模糊状态反馈控制器,以保证闭环系统的随机稳定。此外,在这方面,通过考虑一个新的模糊Lyapunov-Krasovskii泛函(LKF)与自由加权矩阵相结合,包含模相关的非积分项,使得得到的系统具有期望性能的随机稳定。其次,利用隶属函数建立了极点位置法(PPM)。此外,为了使控制器设计更加方便,所设计的控制器不需要与模型共享模糊规则和隶属函数的数量。用同步电机参数作为仿真结果,验证了所提算法的有效性。
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来源期刊
CiteScore
3.50
自引率
16.70%
发文量
0
期刊介绍: The two-monthly Iranian Journal of Fuzzy Systems (IJFS) aims to provide an international forum for refereed original research works in the theory and applications of fuzzy sets and systems in the areas of foundations, pure mathematics, artificial intelligence, control, robotics, data analysis, data mining, decision making, finance and management, information systems, operations research, pattern recognition and image processing, soft computing and uncertainty modeling. Manuscripts submitted to the IJFS must be original unpublished work and should not be in consideration for publication elsewhere.
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