Output Feedback Control for T-S Fuzzy Markov Jump Systems Subjected to Parameter-Dependent Dissipative Performance

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Jian Wang, Jiuxiang Dong
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引用次数: 0

Abstract

This article is committed to the matter of asynchronous dynamic output feedback control for a class of Takagi–Sugeno fuzzy Markov jump systems. The principal concept is to construct a high-level Markovian process governed controller under an asynchronous event-triggered scheme and to develop a time-varying dissipative index. As a salient feature that distinguishes it from the reported works with the common index, this paper firstly proposes a dissipative index with mode and membership functions-dependence. Secondly, governed by the high-level Markovian process, the asynchronous dynamic output feedback controller is constructed under the mode-dependent partitioned regions. Meanwhile, to mitigate the communication pressure, an asynchronous dynamic event-triggered mechanism is investigated with making allowances for the inaccessible system mode. What's more, as a first effort, this article presents a unified framework for co-designing the strategy of asynchronous event generators and controllers. This is done under the proposed performance index and the mode-dependent fuzzy state-space partitions, resulting in more design flexibility. Based on the mode and region-dependent Lyapunov functional, sufficient criteria are obtained to ensure the proposed dissipative performance and stochastic stability. Eventually, the practicabilities and advantages of the theoretic results are illustrated by a modified tunnel diode circuit model.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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