Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic

Weijiang Zheng; Bing Zhu; Xianming Ye; Zongyu Zuo

doi:10.1016/j.ifacol.2023.10.302

Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic

Weijiang Zheng^*
, Bing Zhu^*
, Xianming Ye
, Zongyu Zuo^*

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University
University of Pretoria

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

Abstract

In this paper, a stochastic model predictive control (MPC) is proposed to design a non-pharmacutical policy to control and prevent the COVID-19 pandemic. The system dynamics of COVID-19 is described by a stochastic SEIHR model subject to practical constraints, and the model is proved to be feedback linearizable. A stochastic Control Lyapunov-Barrier Function (CLBF) is constructed for the feedback linearizable system. Constraints on hospitalized individuals are regarded as the unsafe region to construct the corresponding stochastic CLBF. In the proposed stochastic MPC, the stochastic CLBF constraints are applied to improve the overall performance on controlling and preventing the epidemic. Both theoretical proof and simulation results imply that, with the CLBF-based stochastic MPC, the proposed policy is effective in controlling and preventing COVID-19 pandemic.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Publisher	Elsevier B.V.
Pages	6531-6536
Number of pages	6
Edition	2
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.302
State	Published - 1 Jul 2023
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Publication series

Name	IFAC-PapersOnLine
Number	2
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	22nd IFAC World Congress
Country/Territory	Japan
City	Yokohama
Period	9/07/23 → 14/07/23

Keywords

COVID-19
Model predictive control
control Lyapunov-barrier function
feedback linearizable systems
stochastic systems

Access to Document

10.1016/j.ifacol.2023.10.302

Cite this

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title = "Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic",

abstract = "In this paper, a stochastic model predictive control (MPC) is proposed to design a non-pharmacutical policy to control and prevent the COVID-19 pandemic. The system dynamics of COVID-19 is described by a stochastic SEIHR model subject to practical constraints, and the model is proved to be feedback linearizable. A stochastic Control Lyapunov-Barrier Function (CLBF) is constructed for the feedback linearizable system. Constraints on hospitalized individuals are regarded as the unsafe region to construct the corresponding stochastic CLBF. In the proposed stochastic MPC, the stochastic CLBF constraints are applied to improve the overall performance on controlling and preventing the epidemic. Both theoretical proof and simulation results imply that, with the CLBF-based stochastic MPC, the proposed policy is effective in controlling and preventing COVID-19 pandemic.",

keywords = "COVID-19, Model predictive control, control Lyapunov-barrier function, feedback linearizable systems, stochastic systems",

author = "Weijiang Zheng and Bing Zhu and Xianming Ye and Zongyu Zuo",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",

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language = "英语",

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publisher = "Elsevier B.V.",

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Zheng, W, Zhu, B, Ye, X & Zuo, Z 2023, Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic. in H Ishii, Y Ebihara, J Imura & M Yamakita (eds), IFAC-PapersOnLine. 2 edn, IFAC-PapersOnLine, no. 2, vol. 56, Elsevier B.V., pp. 6531-6536, 22nd IFAC World Congress, Yokohama, Japan, 9/07/23. https://doi.org/10.1016/j.ifacol.2023.10.302

Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic. / Zheng, Weijiang; Zhu, Bing; Ye, Xianming et al.
IFAC-PapersOnLine. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. 2. ed. Elsevier B.V., 2023. p. 6531-6536 (IFAC-PapersOnLine; Vol. 56, No. 2).

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

TY - GEN

T1 - Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic

AU - Zheng, Weijiang

AU - Zhu, Bing

AU - Ye, Xianming

AU - Zuo, Zongyu

PY - 2023/7/1

Y1 - 2023/7/1

N2 - In this paper, a stochastic model predictive control (MPC) is proposed to design a non-pharmacutical policy to control and prevent the COVID-19 pandemic. The system dynamics of COVID-19 is described by a stochastic SEIHR model subject to practical constraints, and the model is proved to be feedback linearizable. A stochastic Control Lyapunov-Barrier Function (CLBF) is constructed for the feedback linearizable system. Constraints on hospitalized individuals are regarded as the unsafe region to construct the corresponding stochastic CLBF. In the proposed stochastic MPC, the stochastic CLBF constraints are applied to improve the overall performance on controlling and preventing the epidemic. Both theoretical proof and simulation results imply that, with the CLBF-based stochastic MPC, the proposed policy is effective in controlling and preventing COVID-19 pandemic.

AB - In this paper, a stochastic model predictive control (MPC) is proposed to design a non-pharmacutical policy to control and prevent the COVID-19 pandemic. The system dynamics of COVID-19 is described by a stochastic SEIHR model subject to practical constraints, and the model is proved to be feedback linearizable. A stochastic Control Lyapunov-Barrier Function (CLBF) is constructed for the feedback linearizable system. Constraints on hospitalized individuals are regarded as the unsafe region to construct the corresponding stochastic CLBF. In the proposed stochastic MPC, the stochastic CLBF constraints are applied to improve the overall performance on controlling and preventing the epidemic. Both theoretical proof and simulation results imply that, with the CLBF-based stochastic MPC, the proposed policy is effective in controlling and preventing COVID-19 pandemic.

KW - COVID-19

KW - Model predictive control

KW - control Lyapunov-barrier function

KW - feedback linearizable systems

KW - stochastic systems

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PB - Elsevier B.V.

T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

Control Lyapunov-Barrier Function Based Stochastic Model Predictive Control for COVID-19 Pandemic

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