Nonlinear Model Predictive Control for the Suppression of the COVID-19 Pandemic Based on an Agent-Based Model

被引：1

作者：

Niu, Yue ^{[1
]}

Nagamune, Ryozo ^{[1
]}

机构：

[1] Univ British Columbia, Fac Appl Sci, Dept Mech Engn, 6250 Appl Sci Lane, Vancouver, BC V6T 1Z4, Canada

来源：

2022 IEEE INTERNATIONAL SYMPOSIUM ON ADVANCED CONTROL OF INDUSTRIAL PROCESSES (ADCONIP 2022) | 2022年

基金：

加拿大自然科学与工程研究理事会;

关键词：

D O I：

10.1109/AdCONIP55568.2022.9894144

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, Nonlinear Model Predictive Control (NMPC) is used with an Agent-Based Model (ABM) to find optimal control measures in containing the COVID-19 pandemic. The control measures are designed so that hospitals can maintain some level of available beds, and at the same time, the economy is minimally adversely impacted. It is shown in simulations that NMPC is superior to on-off control and Proportional (P) control, by offering the least economic impact while maintaining the number of hospitalized patients in normal wards and Intensive Care Units (ICUs) below their capacities.

引用

页码：138 / 143

页数：6

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