Neural-based prescribed-time consensus control for multiagent systems via dynamic memory event-triggered mechanism  

作　　者：Xiaohong ZHENG Hui MA Qi ZHOU Hongyi LI 

机构地区：[1]School of Automation,Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control,and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control,Guangdong University of Technology,Guangzhou,510006,China [2]School of Mathematics and Statistics,Guangdong University of Technology,Guangzhou,510006,China [3]College of Electronic and Information Engineering and the Chongqing Key Laboratory of Generic Technology and System of Service Robots,Southwest University,Chongqing,400715,China

出　　处：《Science China(Technological Sciences)》2025年第3期217-227,共11页中国科学(技术科学英文版)

基　　金：partially supported by the National Natural Science Foundation of China(Grant Nos.62033003,62373113,62203119);the Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023A1515011527,2023B1515120010)。

摘　　要：This work investigates the implementation of distributed prescribed-time neural network(NN)control for nonlinear multiagent systems(MASs)using a dynamic memory event-triggered mechanism(DMETM).First,it introduces a composite learning technique in NN control.This method leverages the prediction error within the NN update law to enhance the accuracy of the unknown nonlinearity estimation.Subsequently,by introducing a time-varying transformation,the study establishes a distributed prescribed-time control algorithm.The notable feature of this algorithm is its ability to predetermine the convergence time independently of initial conditions or control parameters.Moreover,the DMETM is established to reduce the actuation frequency of the controller.Unlike the conventional memoryless dynamic event-triggered mechanism,the DMETM incorporates a memory term to further increase triggering intervals.Utilizing a distributed estimator for the leader,the DMETM-based NN prescribed-time controller is designed in a fully distributed manner,which guarantees that all signals in the closed-loop system remain bounded within the prescribed time.Finally,simulation results are presented to validate the effectiveness of the proposed algorithm.

关 键 词：consensus control composite learning control dynamic memory event-triggered control prescribed-time control multiagent systems(MASs) 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]