文献类型：期刊文献

英文题名：Quantized Iterative Learning Control for Nonlinear Multi-Agent Systems with Initial State Error

作者：Zhang, Ting[1,2]; Li, Ning[1]; Chen, Jiaxi[2]

第一作者：Zhang, Ting

机构：[1] School of Mathematics and Information Science, Henan University of Economics and Law, Zhengzhou, 450046, China; [2] School of Mathematics and Statistics, Xidian University, Xi’an, 710126, China

第一机构：河南财经政法大学数学与信息科学学院

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230304596)

语种：英文

外文关键词：Digital communication systems - Iterative methods - Learning algorithms - Learning systems - Quantization (signal) - Two term control systems

摘要：This paper proposed a consensus problem for nonlinear multi-agent systems (MAS) with logarithmic quantization and arbitrary initial states. By introducing the quantization, digital communication between signals is realized and the requirement of utilizing the digital channel more effective is also achieved. In order to eliminate the initial state errors introduced by the assumption of any initial value for every agent and to avoid the complexity for the common method of initial-state learning, a new method of control inputs compensationvia iterative learning control (ILC) is introduced in this paper and the accurate tracking over the entire time period can be obtained asymptotically by the analysis of convergence. The effectiveness of the designed protocol is verified by some numerical simulations. ? 2023, The Authors. All rights reserved.