基于数据驱动技术的非线性时变多智能体系统一致性研究

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

Information Sciences Pub Date : 2025-03-04 DOI:10.1016/j.ins.2025.122052

Yuanshan Liu, Yude Xia

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引用次数: 0

摘要

本文采用数据驱动技术研究了具有固定拓扑的非线性离散时变多智能体系统的领导-随从一致性的鲁棒控制问题。首先，建立了采样数据的相关符号定义，然后介绍了图论和系统模型。由于数据驱动算法需要线性系统，因此每个非线性子系统都是线性化的。然后，根据控制原理设计分布式控制器，保证多智能体的一致性。此外，采用数据驱动方法推导了控制器增益矩阵，并通过求解非线性矩阵不等式从理论上验证了其可行性。最后，数值模拟验证了该方法在实现鲁棒领导-追随者共识控制方面的有效性。

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Investigation of consensus for nonlinear time-varying multiagent systems via data-driven techniques

This paper employs data-driven techniques to investigate the robustness control of leader-follower consensus in nonlinear discrete-time time-varying multiagent systems with fixed topology. Initially, pertinent symbolic definitions for sampled data are established, followed by an introduction to graph theory and system models. As data-driven algorithms necessitate linear systems, each nonlinear subsystem is linearized. Subsequently, distributed controllers are designed based on control principles to ensure multi-agent consensus. Additionally, the controller gain matrix is derived via a data-driven method, with its feasibility theoretically verified by solving nonlinear matrix inequalities. Finally, numerical simulations validate the efficacy of this approach for achieving robust leader-follower consensus control.

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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.