具有多个领导者的奇异多代理系统的数据采样时变形成。

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2024-10-31 DOI:10.1016/j.neunet.2024.106843

Fenglan Sun , Xuemei Yu , Wei Zhu , Jürgen Kurths

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引用次数: 0

摘要

本文研究了具有多个领导者的采样数据下奇异多代理系统的时变编队问题。首先，本文提出了一种数据采样时变编队控制协议，跟随者之间的通信仅发生在采样时刻，这可以大大节省控制器的通信能量。其次，提供了编队函数可行性的必要条件和充分条件。此外，还提出了一种方法来设计具有多个领导者的采样数据下的编队跟踪控制。最后，数值模拟验证了理论结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Data-sampled time-varying formation for singular multi-agent systems with multiple leaders

The time-varying formation problem of singular multi-agent systems under sampled data with multiple leaders is investigated in this paper. Firstly, a data-sampled time-varying formation control protocol is proposed in the current study where the communication among followers merely occurred at sampling instants, which can save the controller communication energy significantly. Secondly, necessary and sufficient conditions for the feasibility of the formation function are provided. In addition, an approach is presented to design the formation tracking control under sampled data with multiple leaders. Finally, numerical simulations validate the efficacy of the theoretical results.

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来源期刊

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.