Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems

Runmin Yang; Rongni Yang; Renjie Ma; Yanzheng Zhu; Peng Shi

doi:10.1109/ICMLC63072.2024.10935104

Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems

Runmin Yang
, Rongni Yang
, Renjie Ma
, Yanzheng Zhu
, Peng Shi

School of Mechatronics Engineering

Shandong University
Shandong University of Science and Technology
University of Adelaide

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

Abstract

This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach.

Original language	English
Title of host publication	Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024
Publisher	IEEE Computer Society
Pages	559-564
Number of pages	6
ISBN (Electronic)	9798331528041
DOIs	https://doi.org/10.1109/ICMLC63072.2024.10935104
State	Published - 2024
Event	23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024 - Hybrid, Miyazaki, Japan Duration: 20 Sep 2024 → 23 Sep 2024

Publication series

Name	Proceedings - International Conference on Machine Learning and Cybernetics
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024
Country/Territory	Japan
City	Hybrid, Miyazaki
Period	20/09/24 → 23/09/24

Keywords

Data-driven control
Roesser model
S-procedure
Two-dimensional (2-D) systems

Access to Document

10.1109/ICMLC63072.2024.10935104

Cite this

Yang, R., Yang, R., Ma, R., Zhu, Y., & Shi, P. (2024). Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems. In Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024 (pp. 559-564). (Proceedings - International Conference on Machine Learning and Cybernetics). IEEE Computer Society. https://doi.org/10.1109/ICMLC63072.2024.10935104

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title = "Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems",

abstract = "This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach.",

keywords = "Data-driven control, Roesser model, S-procedure, Two-dimensional (2-D) systems",

author = "Runmin Yang and Rongni Yang and Renjie Ma and Yanzheng Zhu and Peng Shi",

note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024 ; Conference date: 20-09-2024 Through 23-09-2024",

year = "2024",

doi = "10.1109/ICMLC63072.2024.10935104",

language = "英语",

series = "Proceedings - International Conference on Machine Learning and Cybernetics",

publisher = "IEEE Computer Society",

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booktitle = "Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024",

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Yang, R, Yang, R, Ma, R, Zhu, Y & Shi, P 2024, Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems. in Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024. Proceedings - International Conference on Machine Learning and Cybernetics, IEEE Computer Society, pp. 559-564, 23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024, Hybrid, Miyazaki, Japan, 20/09/24. https://doi.org/10.1109/ICMLC63072.2024.10935104

Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems. / Yang, Runmin; Yang, Rongni; Ma, Renjie et al.
Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024. IEEE Computer Society, 2024. p. 559-564 (Proceedings - International Conference on Machine Learning and Cybernetics).

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

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PY - 2024

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N2 - This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach.

AB - This paper studies the stabilization problem of discrete-time two-dimensional (2-D) systems represented by Roesser based on available data. First of all, based on the pre-collected input-state data, the original system is transformed into a data-based form. Then a set is established to represent all systems capable of generating the given data, thereby indirectly stabilizing the original system by stabilizing all systems within this set. Next, by utilizing the S-procedure, sufficient conditions are established for the resultant closed-loop systems that incorporate a state feedback controller based on collected data. Finally, an example is given to demonstrate the effectiveness of the proposed approach.

KW - Data-driven control

KW - Roesser model

KW - S-procedure

KW - Two-dimensional (2-D) systems

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BT - Proceedings of 2024 International Conference on Machine Learning and Cybernetics, ICMLC 2024

PB - IEEE Computer Society

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Y2 - 20 September 2024 through 23 September 2024

ER -

Data-based Stabilization of Unknown Discrete-time 2-D Roesser Systems

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