Model-Free Adaptive Iterative Learning From Communicable Agents for Nonlinear Networks Consensus

被引：5

作者：

Sun, Shiyong ^{[1
]}

Chi, Ronghu ^{[1
]}

Liu, Yang ^{[1
]}

Lin, Na ^{[1
]}

机构：

[1] Qingdao Univ Sci & Technol, Coll Automat & Elect Engn, Qingdao 266061, Peoples R China

来源：

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS | 2023年 / 9卷

基金：

中国国家自然科学基金;

关键词：

Nonaffine nonlinear multiagent systems; random initial condition; consensus learning; spatial data model; MULTIAGENT SYSTEMS; TRACKING CONTROL; ALGORITHMS; PROTOCOLS; DESIGN;

D O I：

10.1109/TSIPN.2023.3292996

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work reconsiders the consensus tracking issue from a new viewpoint of learning from communicable agents in a strongly connected nonlinear nonaffine multiagent system (MAS). First, we present a communicable-agent-based linear data model (CA-LDM) for describing the input-output (I/O) dynamics between an agent and its neighbors. Meanwhile, an iterative adaptive method is designed to update the CA-LDM by employing I/O data. Then, a communicable-agent-based model-free adaptive iterative learning consensus (CA-MFAILC) scheme is developed by learning the spatial behaviour of MAS and the behaviour of the agent itself. The proposed algorithm does not require the same initial conditions, such that it is easy to be applied to the real applications. Besides, the presented CA-MFAILC does not use the model information, but being a data-driven method. The rigorous analysis along with the simulation study illustrates the effectiveness of the CA-MFAILC.

引用

页码：458 / 467

页数：10