Xiaohong Zheng, Xiao‐Meng Li, Wenbin Xiao, Qi Zhou, Renquan Lu
{"title":"NN-based Fixed-Time Tracking Control for Multi-Agent Systems With Input Delays","authors":"Xiaohong Zheng, Xiao‐Meng Li, Wenbin Xiao, Qi Zhou, Renquan Lu","doi":"10.1109/ICCSS53909.2021.9722024","DOIUrl":null,"url":null,"abstract":"This article discusses a fixed-time consensus tracking control problem for multi-agent systems (MASs) suffering from input delays. First, the Pade approximation technique is employed to deal with input delays. Second, unknown nonlinearities in MASs are reconstructed by command filtering technique and neural network (NN). Convex optimization technique is used to design NN weight update law. To guarantee the transient performance of MASs, fixed-time control is utilized, while the resulting singularity problem is solved by curve fitting method. Under the fixed-time stability criterion and Lyapunov stability theorem, it is shown that all signals of the closed-loop system are bounded in fixed time. Finally, the validity of the presented algorithm is checked by simulation.","PeriodicalId":435816,"journal":{"name":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSS53909.2021.9722024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article discusses a fixed-time consensus tracking control problem for multi-agent systems (MASs) suffering from input delays. First, the Pade approximation technique is employed to deal with input delays. Second, unknown nonlinearities in MASs are reconstructed by command filtering technique and neural network (NN). Convex optimization technique is used to design NN weight update law. To guarantee the transient performance of MASs, fixed-time control is utilized, while the resulting singularity problem is solved by curve fitting method. Under the fixed-time stability criterion and Lyapunov stability theorem, it is shown that all signals of the closed-loop system are bounded in fixed time. Finally, the validity of the presented algorithm is checked by simulation.