具有参数不确定性的非线性多智能体系统的鲁棒采样数据模糊一致控制

IF 6.8 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2025-07-22 DOI:10.1016/j.ins.2025.122519

Yong Hoon Jang , Seunghoon Lee , Han Sol Kim

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引用次数: 0

摘要

针对包含参数不确定性的非线性多智能体系统，提出了一种采样数据模糊一致控制技术。首先，将具有参数不确定性的非线性MAS建模为Takagi-Sugeno （T-S）模糊系统。然后，通过图论的应用，引入误差动力学，确保领导-跟随共识。为了得到保证鲁棒镇定的松弛充分条件，本研究引入了改进的隶属函数相关H∞准则和一个双边环泛函。得到的鲁棒控制器设计条件以线性矩阵不等式（lmi）的形式表示。最后通过数值仿真实例说明了所提设计方法的优越性和可行性。

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Robust sampled-data fuzzy consensus control for nonlinear multi-agent systems with parametric uncertainties

This study proposes a sampled-data fuzzy consensus control technique for nonlinear multi-agent systems (MAS) containing parametric uncertainties. First, the nonlinear MAS with parametric uncertainties is modeled as a Takagi–Sugeno (T–S) fuzzy system. And then, the study introduces error dynamics, ensuring leader-following consensus, through the application of graph theory. To obtain relaxed sufficient conditions guaranteeing robust stabilization, this research incorporates improved membership function-dependent (MFD)

H_{\infty}

criteria and a two-sided looped-functional. The resulting robust controller design conditions are expressed in the form of linear matrix inequalities (LMIs). Finally, numerical simulation examples are given to illustrate the superior performance and feasibility of the proposed design technique.

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.