Adaptive iterative learning control for high-order nonlinear systems with different types of uncertainties

被引：1
作者：
Li, Xuefang [1 ]
Chen, Yanfang [1 ]
Sun, Hui-Jie [2 ]
Liu, Wanquan [1 ,3 ]
机构：
[1] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen, Guangdong, Peoples R China
[2] Sun Yat Sen Univ, Sch Aeronaut & Astronaut, Shenzhen Campus, Shenzhen 518107, Guangdong, Peoples R China
[3] Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus,66 Gongchang Rd, Shenzhen 518107, Guangdong, Peoples R China
来源：
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2024年 / 34卷 / 08期
基金：
中国国家自然科学基金;
关键词：
adaptive iterative learning control; composite energy function; high-order fully actuated systems; nonlinear systems; uncertainty; TRACKING;
D O I：
10.1002/rnc.7281
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The present work aims at investigating the adaptive iterative learning control (AILC) design for uncertain high-order fully actuated nonlinear systems. In order to show the design principles, three types of nonlinear systems are considered, including systems with just parametric uncertainty, systems with both parametric and input distribution uncertainties as well as systems with both parametric uncertainty and an unknown control gain matrix. For different systems, the corresponding AILC schemes are proposed with different techniques in dealing with various system uncertainties, for which the convergence analysis are conducted rigorously based on the composite energy function. The effectiveness of the proposed AILC strategies is verified through numerical simulations.
引用
收藏
页码：5399 / 5418
页数：20
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