A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems

Yuanqiang ZHOU; Xiaopeng TANG; Furong GAO; Xin LAI; Dewei LI; Weiguo MA

doi:10.1109/ICA-SYMP56348.2023.10044931

A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems

Yuanqiang ZHOU, Xiaopeng TANG, Furong GAO, Xin LAI, Dewei LI, Weiguo MA

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

1 Citation (Scopus)

Abstract

This paper discusses optimal batch-to-batch (B2B) control problems and presents a gradient descent method solution for unknown linear batch process systems. Using historical process data, we design a model-free method for B2B optimization that eliminates the need for model information about the system. By using quadratic programming (QP) to formulate the optimal controller design, we first present the optimal iterative learning control (ILC) results. Next, using the gradient descent method, we replace the uncertain term with the actual measurements and develop a new ILC approach based on convex hull representations of uncertain realizations. As compared to the norm-optimal ILC, our proposed ILC can guarantee superior performance with reasonably selected parameters. Finally, we demonstrate our design with an illustrative numerical example.

Original language	English
Title of host publication	Proceeding of 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	87-90
Number of pages	4
ISBN (Electronic)	9781665473538
DOIs	https://doi.org/10.1109/ICA-SYMP56348.2023.10044931
Publication status	Published - 2023
Externally published	Yes
Event	3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023 - Bangkok, Thailand Duration: 18 Jan 2023 → 20 Jan 2023

Conference

Conference	3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023
Country/Territory	Thailand
City	Bangkok
Period	18/01/23 → 20/01/23

Bibliographical note

Funding Information:
This work was supported in part by the Hong Kong Research Grant Council under grant 16208520; in part by the Foshan-HKUST Project under grant FSUST19-FYTRI01; in part by the Guangdong scientific and technological project (202002030323 and 2014B050505002); and in part by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2122-6S06).

Publisher Copyright:
© 2023 IEEE.

Keywords

data-driven control
iterative learning control (ILC)
optimization
Process control

Access to Document

10.1109/ICA-SYMP56348.2023.10044931

Cite this

ZHOU, Y., TANG, X., GAO, F., LAI, X., LI, D., & MA, W. (2023). A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems. In Proceeding of 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023 (pp. 87-90). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICA-SYMP56348.2023.10044931

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title = "A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems",

abstract = "This paper discusses optimal batch-to-batch (B2B) control problems and presents a gradient descent method solution for unknown linear batch process systems. Using historical process data, we design a model-free method for B2B optimization that eliminates the need for model information about the system. By using quadratic programming (QP) to formulate the optimal controller design, we first present the optimal iterative learning control (ILC) results. Next, using the gradient descent method, we replace the uncertain term with the actual measurements and develop a new ILC approach based on convex hull representations of uncertain realizations. As compared to the norm-optimal ILC, our proposed ILC can guarantee superior performance with reasonably selected parameters. Finally, we demonstrate our design with an illustrative numerical example.",

keywords = "data-driven control, iterative learning control (ILC), optimization, Process control",

author = "Yuanqiang ZHOU and Xiaopeng TANG and Furong GAO and Xin LAI and Dewei LI and Weiguo MA",

note = "Funding Information: This work was supported in part by the Hong Kong Research Grant Council under grant 16208520; in part by the Foshan-HKUST Project under grant FSUST19-FYTRI01; in part by the Guangdong scientific and technological project (202002030323 and 2014B050505002); and in part by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2122-6S06). Publisher Copyright: {\textcopyright} 2023 IEEE.; 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023 ; Conference date: 18-01-2023 Through 20-01-2023",

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doi = "10.1109/ICA-SYMP56348.2023.10044931",

language = "English",

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ZHOU, Y, TANG, X, GAO, F, LAI, X, LI, D & MA, W 2023, A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems. in Proceeding of 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023. Institute of Electrical and Electronics Engineers Inc., pp. 87-90, 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023, Bangkok, Thailand, 18/01/23. https://doi.org/10.1109/ICA-SYMP56348.2023.10044931

A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems. / ZHOU, Yuanqiang; TANG, Xiaopeng; GAO, Furong et al.
Proceeding of 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 87-90.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems

AU - ZHOU, Yuanqiang

AU - TANG, Xiaopeng

AU - GAO, Furong

AU - LAI, Xin

AU - LI, Dewei

AU - MA, Weiguo

N1 - Funding Information: This work was supported in part by the Hong Kong Research Grant Council under grant 16208520; in part by the Foshan-HKUST Project under grant FSUST19-FYTRI01; in part by the Guangdong scientific and technological project (202002030323 and 2014B050505002); and in part by the Hong Kong RGC Postdoctoral Fellowship Scheme (PDFS2122-6S06). Publisher Copyright: © 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - This paper discusses optimal batch-to-batch (B2B) control problems and presents a gradient descent method solution for unknown linear batch process systems. Using historical process data, we design a model-free method for B2B optimization that eliminates the need for model information about the system. By using quadratic programming (QP) to formulate the optimal controller design, we first present the optimal iterative learning control (ILC) results. Next, using the gradient descent method, we replace the uncertain term with the actual measurements and develop a new ILC approach based on convex hull representations of uncertain realizations. As compared to the norm-optimal ILC, our proposed ILC can guarantee superior performance with reasonably selected parameters. Finally, we demonstrate our design with an illustrative numerical example.

AB - This paper discusses optimal batch-to-batch (B2B) control problems and presents a gradient descent method solution for unknown linear batch process systems. Using historical process data, we design a model-free method for B2B optimization that eliminates the need for model information about the system. By using quadratic programming (QP) to formulate the optimal controller design, we first present the optimal iterative learning control (ILC) results. Next, using the gradient descent method, we replace the uncertain term with the actual measurements and develop a new ILC approach based on convex hull representations of uncertain realizations. As compared to the norm-optimal ILC, our proposed ILC can guarantee superior performance with reasonably selected parameters. Finally, we demonstrate our design with an illustrative numerical example.

KW - data-driven control

KW - iterative learning control (ILC)

KW - optimization

KW - Process control

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DO - 10.1109/ICA-SYMP56348.2023.10044931

M3 - Conference paper (refereed)

AN - SCOPUS:85149628576

SP - 87

EP - 90

BT - Proceeding of 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023

Y2 - 18 January 2023 through 20 January 2023

ER -

ZHOU Y, TANG X, GAO F, LAI X, LI D, MA W. A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems. In Proceeding of 2023 3rd International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 87-90 doi: 10.1109/ICA-SYMP56348.2023.10044931

A Gradient Descent Method for Optimal Batch-to-Batch Control of Unknown Linear Systems

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Keywords

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