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

School of Automation Science and Electrical Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	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
Number of pages	6
ISBN (Electronic)	9781538626184
DOIs	https://doi.org/10.1109/DDCLS.2018.8516044
State	Published - 30 Oct 2018
Event	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018 - Enshi, Hubei Province, China Duration: 25 May 2018 → 27 May 2018

Publication series

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

Conference

Conference	7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018
Country/Territory	China
City	Enshi, Hubei Province
Period	25/05/18 → 27/05/18

Keywords

Data-based
Iterative learning control
Locally Lipschitz nonlinearity

Access to Document

10.1109/DDCLS.2018.8516044

Cite this

Zhang, J., & Meng, D. (2018). Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. In Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018 (pp. 908-913). Article 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. pp. 908-913 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

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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",

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doi = "10.1109/DDCLS.2018.8516044",

language = "英语",

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

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Zhang, J & Meng, D 2018, Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. in 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., pp. 908-913, 7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018, Enshi, Hubei Province, China, 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. p. 908-913 8516044 (Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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DO - 10.1109/DDCLS.2018.8516044

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 25 May 2018 through 27 May 2018

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Zhang J, Meng D. Iterative learning control for continuous-time systems with locally lipschitz nonlinearity and input saturation. In Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 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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