Adaptive iterative learning consensus control for second-order multi-agent systems with unknown control gains

被引：19
|
作者：
Li, Guilu [1 ]
Ren, Chang-E [1 ]
Chen, C. L. Philip [2 ,3 ]
Shi, Zhiping [1 ]
机构：
[1] Capital Normal Univ, Sch Informat Engn, Beijing, Peoples R China
[2] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Peoples R China
[3] Dalian Maritime Univ, Dalian, Peoples R China
来源：
NEUROCOMPUTING | 2020年 / 393卷 / 393期
基金：
中国国家自然科学基金;
关键词：
Unknown control gains; Multi-agent systems; Adaptive consensus control; Iterative learning; Neural networks; NONLINEAR-SYSTEMS; CONTROL DIRECTIONS; DESIGN; ALGORITHMS; LEADERLESS;
D O I：
10.1016/j.neucom.2020.01.108
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We respond to the consensus control algorithms for second-order non-linear MAS with unknown control gains in the finite time interval by adopting iterative learning control (ILC) methods in this paper. Compared to the current findings, the control gains in the proposed method are unknown functions with unknown and non-identical signs. The topology graph between the follower agents is an undirected connected graph. By referencing the Nussbaum-type function and the neural networks, the adaptive control algorithms are intended to cope with the consensus control between the agents on the limited time interval. Simulation results illustrate the efficacy of the raised algorithms. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页码：15 / 26
页数：12
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