Dynamic event-triggered data-driven iterative learning bipartite tracking control for nonlinear MASs with prescribed performance  

作　　者：Tao SHI Wei-Wei CHE 

机构地区：[1]School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China [2]State Key Laboratory of Synthetical Automation for Process Industries,Northeastern University,Shenyang 110819,China [3]College of Information Science and Engineering,Northeastern University,Shenyang 110819,China

出　　处：《Science China(Information Sciences)》2025年第1期289-302,共14页中国科学(信息科学)(英文版)

基　　金：supported by National Natural Science Foundation of China(Grant Nos.U1966202,61873338,62273191,62233015);Taishan Scholars(Grant No.tsqn201812052);Natural Science Foundation of Shandong Province(Grant No.ZR2020KF034)。

摘　　要：This article proposes a distributed dynamic event-triggered data-driven iterative learning control(DET-DDILC)scheme under a predefined performance to tackle the bipartite tracking control problem for multiagent systems(MASs).An improved dynamic linearization technique is utilized to convert the nonlinear MASs into an iterative linear data model.First,a peer-to-peer mapping function is introduced to map the constrained distributed system output homeomorphism to an unconstrained one.In addition,a DET mechanism based on a time-iteration-varying function is devised to conserve network communication resources.Based on the unconstrained transformation and the designed DET mechanism,the DET-DDILC algorithm is devised to ensure that the bipartite tracking performance of MASs can be within the preset range.Finally,the effectiveness and feasibility of the designed control scheme are demonstrated via a simulation case by a comparison.

关 键 词：bipartite tracking control prescribed performance event-triggered iterative learning control multiagent systems 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]