{"title":"具有多个领导者的奇异多代理系统的数据采样时变形成。","authors":"Fenglan Sun , Xuemei Yu , Wei Zhu , Jürgen Kurths","doi":"10.1016/j.neunet.2024.106843","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106843"},"PeriodicalIF":6.0000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Data-sampled time-varying formation for singular multi-agent systems with multiple leaders\",\"authors\":\"Fenglan Sun , Xuemei Yu , Wei Zhu , Jürgen Kurths\",\"doi\":\"10.1016/j.neunet.2024.106843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":49763,\"journal\":{\"name\":\"Neural Networks\",\"volume\":\"181 \",\"pages\":\"Article 106843\"},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0893608024007676\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024007676","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
