Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition

Mouquan Shen; Xingzheng Wu; Ju H. Park; Yang Yi; Yonghui Sun

doi:10.1109/TNNLS.2021.3135504

Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition

Mouquan Shen, Xingzheng Wu, Ju H. Park, Yang Yi, Yonghui Sun

电气工程与控制科学学院

科研成果: 期刊稿件 › 文章 › 同行评审

34 引用（Scopus）

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This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

源语言	英语
页（从-至）	6670-6676
页数	7
期刊	IEEE Transactions on Neural Networks and Learning Systems
卷	34
期	9
DOI	https://doi.org/10.1109/TNNLS.2021.3135504
出版状态	已出版 - 1 9月 2023

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AB - This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

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Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition

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