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

COLLEGE OF ELECTRICAL ENGINEERING AND CONTROL SCIENCE

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	6670-6676
Number of pages	7
Journal	IEEE Transactions on Neural Networks and Learning Systems
Volume	34
Issue number	9
DOIs	https://doi.org/10.1109/TNNLS.2021.3135504
State	Published - 1 Sep 2023

Keywords

Iterative learning control (ILC)
nonidentical trial lengths
tracking control

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10.1109/TNNLS.2021.3135504

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abstract = "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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AU - Sun, Yonghui

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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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Keywords

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