Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation

Jingyao Zhang; Deyuan Meng

doi:10.1109/DDCLS.2018.8516044

Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation

Jingyao Zhang
, Deyuan Meng

自动化科学与电气工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

3 引用（Scopus）

摘要

In this paper, a data-based iterative learning control (ILC) is developed to address the output tracking problem of continuous-time locally Lipschitz nonlinear systems subject to input saturation. Under a data-based ILC update law with saturation, an extended data driven framework is established for the ILC convergence in the presence of locally Lipschitz nonlinearity and input saturation. A relative degree condition and the input-to-state stability are given to ensure the boundedness of the state and the convergence of the output tracking error simultaneously. The simulation demonstrates the effectiveness of the results.

源语言	英语
主期刊名	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018
出版商	Institute of Electrical and Electronics Engineers Inc.
页	908-913
页数	6
ISBN（电子版）	9781538626184
DOI	https://doi.org/10.1109/DDCLS.2018.8516044
出版状态	已出版 - 30 10月 2018
活动	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, 中国期限: 25 5月 2018 → 27 5月 2018

丛书

姓名	Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

会议

会议	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
国家/地区	中国
市	Enshi, Hubei Province
时期	25/05/18 → 27/05/18

访问文件

10.1109/DDCLS.2018.8516044

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhang, J., & Meng, D. (2018). Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. 在 Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 (页码 908-913). 期刊论文 8516044 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DDCLS.2018.8516044

Zhang, Jingyao ; Meng, Deyuan. / Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 页码 908-913 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

@inproceedings{991417a4fa7f459d839341d0178a914f,

title = "Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation",

abstract = "In this paper, a data-based iterative learning control (ILC) is developed to address the output tracking problem of continuous-time locally Lipschitz nonlinear systems subject to input saturation. Under a data-based ILC update law with saturation, an extended data driven framework is established for the ILC convergence in the presence of locally Lipschitz nonlinearity and input saturation. A relative degree condition and the input-to-state stability are given to ensure the boundedness of the state and the convergence of the output tracking error simultaneously. The simulation demonstrates the effectiveness of the results.",

keywords = "Data-based, Iterative learning control, Locally Lipschitz nonlinearity",

author = "Jingyao Zhang and Deyuan Meng",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 ; Conference date: 25-05-2018 Through 27-05-2018",

year = "2018",

month = oct,

day = "30",

doi = "10.1109/DDCLS.2018.8516044",

language = "英语",

series = "Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "908--913",

booktitle = "Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018",

address = "美国",

}

Zhang, J & Meng, D 2018, Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. 在 Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018., 8516044, Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018, Institute of Electrical and Electronics Engineers Inc., 页码 908-913, 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018, Enshi, Hubei Province, 中国, 25/05/18. https://doi.org/10.1109/DDCLS.2018.8516044

Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. / Zhang, Jingyao; Meng, Deyuan.
Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 页码 908-913 8516044 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation

AU - Zhang, Jingyao

AU - Meng, Deyuan

PY - 2018/10/30

Y1 - 2018/10/30

N2 - In this paper, a data-based iterative learning control (ILC) is developed to address the output tracking problem of continuous-time locally Lipschitz nonlinear systems subject to input saturation. Under a data-based ILC update law with saturation, an extended data driven framework is established for the ILC convergence in the presence of locally Lipschitz nonlinearity and input saturation. A relative degree condition and the input-to-state stability are given to ensure the boundedness of the state and the convergence of the output tracking error simultaneously. The simulation demonstrates the effectiveness of the results.

AB - In this paper, a data-based iterative learning control (ILC) is developed to address the output tracking problem of continuous-time locally Lipschitz nonlinear systems subject to input saturation. Under a data-based ILC update law with saturation, an extended data driven framework is established for the ILC convergence in the presence of locally Lipschitz nonlinearity and input saturation. A relative degree condition and the input-to-state stability are given to ensure the boundedness of the state and the convergence of the output tracking error simultaneously. The simulation demonstrates the effectiveness of the results.

KW - Data-based

KW - Iterative learning control

KW - Locally Lipschitz nonlinearity

UR - https://www.scopus.com/pages/publications/85057004332

U2 - 10.1109/DDCLS.2018.8516044

DO - 10.1109/DDCLS.2018.8516044

M3 - 会议稿件

AN - SCOPUS:85057004332

T3 - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

SP - 908

EP - 913

BT - Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018

Y2 - 25 May 2018 through 27 May 2018

ER -

Zhang J, Meng D. Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. 在 Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 页码 908-913. 8516044. (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018). doi: 10.1109/DDCLS.2018.8516044

Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation

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