Global iterative learning control based on fuzzy systems for nonlinear multi-agent systems with unknown dynamics

被引：21
|
作者：
Zhang, Shuai [1 ]
Chen, Jiaxi [2 ]
Bai, Chan [1 ]
Li, Junmin [2 ]
机构：
[1] Xidian Univ, Sci & Technol Antennas & Microwave Lab, Xian 710071, Peoples R China
[2] Xidian Univ, Sch Math & Stat, Xian 710071, Peoples R China
来源：
INFORMATION SCIENCES | 2022年 / 587卷
基金：
中国国家自然科学基金;
关键词：
Adaptive iterative learning control; Multi-agent systems; Fuzzy systems; Global consensus; COORDINATION CONTROL; CONSENSUS CONTROL; TOPOLOGIES; NETWORKS; TRACKING; DESIGN;
D O I：
10.1016/j.ins.2021.12.027
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new global fuzzy iterative learning scheme is proposed for nonlinear multi-agent systems with unknown dynamics. Unlike the traditional design scheme where the fuzzy systems are used as the feedback compensators, the fuzzy systems are used as the feedforward compensators to describe the unknown dynamics, which avoids the restriction on the states of the control systems. In this scheme, we design a hybrid fuzzy adaptive learning controller according to the characteristics of the network structure. On this basis, using the Nussbaum function, this paper extends the above global fuzzy iterative learning scheme to solve the consensus control problem of multi-agent systems with unknown control directions over the iterations. Finally, the effectiveness of the above hybrid learning protocols is verified through simulations. (C) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码：556 / 571
页数：16
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