文献类型：期刊文献

英文题名：Model-Free Formation Control: Multi-Input Iterative Learning Super-Twisting Approach

作者：Xu, Jing[1];Cai, Yunsong[1];Liu, Di[1];Niu, Yugang[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2024

卷号：54

期号：5

起止页码：2765

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20240515485266);WOS:【SCI-EXPANDED(收录号:WOS:001167548300001)】；

基金：No Statement Available

语种：英文

外文关键词：Uncertainty; Iterative methods; Task analysis; Symmetric matrices; Formation control; Time-domain analysis; Robustness; Finite-time prescribed performance; formation control; iteration learning control; singular perturbations; super-twisting control

摘要：This work proposes an economic model-free super twisting control (STWC) algorithm for the FCT of a singularly perturbed MAS. Specifically, the intelligent model-free control framework is designed to be the sum of a MISTWC and an iterative learning control (ILC). First, time scales are artificially introduced into the STWC for the multiagent formation construction, without overestimating the control gains. Then, the input-output data collected from the iterative experiments are used to learn the model of unknown repeated uncertainties, and drive the whole system toward satisfactory consensus tracking performance. By utilizing the epsilon-dependent Lyapunov method, the convergence properties of the STWC-type ILC are rigorously analyzed in both the iteration domain and the time domain. The selection method of the design parameters is also provided. Simulation results validate the effectiveness of the proposed controller in terms of formation construction, trajectory tracking, and robustness to system uncertainties.