集美大学学报（自然科学版）2026，Vol.31Issue(4)：482-491,10.DOI:10.19715/j.jmuzr.2026.04.09
基于高阶采样数据迭代学习的非线性不确定系统跟踪控制
Tracking Control of Nonlinear Uncertain Systems Based on Iterative Learning with High-Order Sampled Data
摘要
Abstract
For a class of systems with uncertainties and whose performance is highly dependent on histori-cal data,traditional first-order iterative learning control(ILC)struggles to effectively utilize historical informa-tion,facing limitations such as insufficient convergence accuracy and difficulties in ensuring stability.To ad-dress these issues,this paper proposes a high-order sampled data ILC strategy.By designing a sampled data mechanism,the data storage and communication requirements are significantly reduced.By introducing a high-order learning law and integrating effective data from multiple historical batches,the system's ability to suppress uncertain disturbances is enhanced.Based on the convergence lemma of linear recursive inequalities and the Bellman-Gronwall lemma,sufficient conditions for the asymptotic convergence of the system are established.Fi-nally,the effectiveness of the proposed method is verified through numerical simulations.关键词
系统跟踪控制/迭代学习控制(ILC)/采样数据/P型学习/收敛性分析Key words
system tracking control/iterstive learning control(ILC)/sampled data/P-type learning/con-vergence analysis分类
数理科学引用本文复制引用
沈彦婷,黄振坤..基于高阶采样数据迭代学习的非线性不确定系统跟踪控制[J].集美大学学报（自然科学版）,2026,31(4):482-491,10.基金项目
福建省自然科学基金(2024J01725) （2024J01725）
