Iterative learning control for fractional-order multi-agent systems

被引：25
|
作者：
Luo, Dahui [1 ]
Wang, JinRong [1 ]
Shen, Dong [2 ]
Feckan, Michal [3 ,4 ]
机构：
[1] Guizhou Univ, Dept Math, Guiyang 550025, Guizhou, Peoples R China
[2] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China
[3] Comenius Univ, Fac Math Phys & Informat, Dept Math Anal & Numer Math, Bratislava 84248, Slovakia
[4] Slovak Acad Sci, Math Inst, Stefanikova 49, Bratislava 81473, Slovakia
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2019年 / 356卷 / 12期
基金：
中国国家自然科学基金;
关键词：
SUFFICIENT CONDITIONS; TRACKING CONTROL; CONSENSUS; DESIGN;
D O I：
10.1016/j.jfranklin.2019.06.001
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we apply iterative learning control to both linear and nonlinear fractional-order multiagent systems to solve consensus tacking problem. Both fixed and iteration-varying communicating graphs are addressed in this paper. For linear systems, a PD alpha-type update law with initial state learning mechanism is introduced by virtue of the memory property of fractional-order derivative. For nonlinear systems, a D-alpha-type update law with forgetting factor and initial state learning is designed. Sufficient conditions for both linear and nonlinear systems are established to guarantee all agents achieving the asymptotic output consensus. Simulation examples are provided to verify the proposed schemes. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：6328 / 6351
页数：24
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