沈阳大学学报（自然科学版）2026，Vol.38Issue(4)：344-352,9.
不确定离散线性奇异系统的迭代学习控制鲁棒收敛性
Robust Convergence of Iterative Learning Control for Uncertain Discrete-Time Linear Singular Systems
摘要
Abstract
A gain-adaptive iterative learning control(GAILC)algorithm was designed for a class of discrete-time linear time-invariant singular systems with system uncertainties.The strategy was based on a learning gain vector derived from the system Markov parameters,and the learning gain vector was solved by minimizing the tracking error vector,effectively improving tracking performance.First,the original two-dimensional singular system was transformed into an algebraic system in the iteration domain using batch processing.Second,an optimization method was employed to design the learning gain of the iterative learning control algorithm,the control algorithm was constructed,and the existence of the optimal learning gain was analyzed.Third,the robust convergence of the proposed control algorithm was analyzed.Finally,the correctness and effectiveness of the proposed method were verified through simulation.关键词
迭代学习控制/单调收敛/范数最优增益/鲁棒性/自适应控制Key words
iterative learning control/monotonic convergence/norm-optimal gain/robustness/adaptive control分类
数理科学引用本文复制引用
杨静,杨轩..不确定离散线性奇异系统的迭代学习控制鲁棒收敛性[J].沈阳大学学报（自然科学版）,2026,38(4):344-352,9.基金项目
陕西省重点研发计划项目(2020GY-072). （2020GY-072）
