参数统计
控制理论(社会学)
平方(代数)
非线性系统
迭代学习控制
趋同(经济学)
计算机科学
多输入多输出
自适应控制
数学优化
鲁棒控制
数学
控制(管理)
人工智能
经济增长
量子力学
统计
物理
频道(广播)
计算机网络
经济
几何学
作者
Xuefang Li,Zhongsheng Hou
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-8
标识
DOI:10.1109/tac.2023.3335243
摘要
In this work, the adaptive iterative learning control (AILC) for a generic class of non-square nonlinear systems is investigated in presence of unknown control gain matrices and non-parametric iteration-varying uncertainties. Differently from the existing approaches, the present work develops a unified, structurally simple and user-friendly AILC method, which is effective to handle nonlinear systems with parametric or non-parametric uncertainties, square or non-square input matrices, known or unknown control directions. From the design point of view, the proposed approach extends the AILC approach to non-square systems with unknown control gain matrices, which contributes significantly not only to refine the theory of AILC, but also to widen its application scope. The convergence of the proposed control algorithms are analysed rigorously by using the composite energy function methodology, and their effectiveness have been verified through an illustrated example.
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