Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework

Shengri Xue; Zhan Li; Yipeng Yang; Liu Yang; Yingxin Yan; Weiyang Lin

doi:10.1109/IECON.2019.8927175

Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework

Shengri Xue
, Zhan Li^*
, Yipeng Yang
, Liu Yang
, Yingxin Yan
, Weiyang Lin

^*Corresponding author for this work

School of Astronautics

School of Astronautics, Harbin Institute of Technology
Harbin University of Science and Technology
Science and Technology on Space Physics Laboratory

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

1 Scopus citations

Abstract

The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
Pages	3755-3760
Number of pages	6
ISBN (Electronic)	9781728148786
DOIs	https://doi.org/10.1109/IECON.2019.8927175
State	Published - Oct 2019
Event	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal Duration: 14 Oct 2019 → 17 Oct 2019

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
Volume	2019-October

Conference

Conference	45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
Country/Territory	Portugal
City	Lisbon
Period	14/10/19 → 17/10/19

Keywords

MPC
multirate sampled-data system
partial least square

Access to Document

10.1109/IECON.2019.8927175

Cite this

Xue, S., Li, Z., Yang, Y., Yang, L., Yan, Y., & Lin, W. (2019). Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 3755-3760). Article 8927175 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IECON.2019.8927175

@inproceedings{27b9ec0341814d6ea1e62af142d47a48,

title = "Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework",

abstract = "The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.",

keywords = "MPC, multirate sampled-data system, partial least square",

author = "Shengri Xue and Zhan Li and Yipeng Yang and Liu Yang and Yingxin Yan and Weiyang Lin",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 ; Conference date: 14-10-2019 Through 17-10-2019",

year = "2019",

month = oct,

doi = "10.1109/IECON.2019.8927175",

language = "英语",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

pages = "3755--3760",

booktitle = "Proceedings",

address = "美国",

}

Xue, S, Li, Z , Yang, Y, Yang, L, Yan, Y & Lin, W 2019, Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework. in Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society., 8927175, IECON Proceedings (Industrial Electronics Conference), vol. 2019-October, IEEE Computer Society, pp. 3755-3760, 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019, Lisbon, Portugal, 14/10/19. https://doi.org/10.1109/IECON.2019.8927175

Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework. / Xue, Shengri; Li, Zhan ; Yang, Yipeng et al.
Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2019. p. 3755-3760 8927175 (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October).

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

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AU - Yang, Yipeng

AU - Yang, Liu

AU - Yan, Yingxin

AU - Lin, Weiyang

PY - 2019/10

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N2 - The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.

AB - The target of this article is to design a data-driven model predictive control algorithm for general multirate sampled-data systems. Multirate sampling widely exists in the industrial process control systems. In this paper, not only sampling periods between inputs and outputs are different, but also periods among inputs or outputs are different from each other. For such the general multirate sampled-data system, we combine the lifting technique and partial least square method to obtain inputs/outputs data sets, which are used for the model regression. An incorporating autoregressive exogenous (ARX) structure model is utilized to model predict. Then we give the principal components cost function for the model predictive control algorithm. Finally, we use example to illustrate the ARX model's precision and the efficiency of our data-driven model predictive control algorithm for general multirate sampled-data systems.

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Model Predictive Control Method for Multirate Sampled-Data System Based on PLS Framework

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Keywords

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