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

作     者：Zhang, Shuai Chen, Jiaxi Bai, Chan Li, Junmin 

作者机构：Xidian Univ Sci & Technol Antennas & Microwave Lab Xian 710071 Peoples R China Xidian Univ Sch Math & Stat Xian 710071 Peoples R China 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2022年第587卷

页      面：556-571页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61603286, 62106186] Fundamental Research Funds for the Central Universities [JB210701] 

主　　题：Adaptive iterative learning control Multi-agent systems Fuzzy systems Global consensus 

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.