Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas
In this work, we present a switching nonlinear model predictive control (NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use maximum likelihood estimation (MLE) to identify model parameters. A dual-hormone AP consists of a continuous glucose monitor (CGM), a control algorithm, an...
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| creator | Reenberg, Asbjørn Thode Ritschel, Tobias K. S Lindkvist, Emilie B Laugesen, Christian Svensson, Jannet Ranjan, Ajenthen G Nørgaard, Kirsten Jørgensen, John Bagterp |
| description | In this work, we present a switching nonlinear model predictive control
(NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use
maximum likelihood estimation (MLE) to identify model parameters. A
dual-hormone AP consists of a continuous glucose monitor (CGM), a control
algorithm, an insulin pump, and a glucagon pump. The AP is designed with a
heuristic to switch between insulin and glucagon as well as state-dependent
constraints. We extend an existing glucoregulatory model with glucagon and
exercise for simulation, and we use a simpler model for control. We test the AP
(NMPC and MLE) using in silico numerical simulations on 50 virtual people with
type 1 diabetes. The system is identified for each virtual person based on data
generated with the simulation model. The simulations show a mean of 89.3% time
in range (3.9-10 mmol/L) and no hypoglycemic events. |
| doi_str_mv | 10.48550/arxiv.2202.13938 |
| format | Article |
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(NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use
maximum likelihood estimation (MLE) to identify model parameters. A
dual-hormone AP consists of a continuous glucose monitor (CGM), a control
algorithm, an insulin pump, and a glucagon pump. The AP is designed with a
heuristic to switch between insulin and glucagon as well as state-dependent
constraints. We extend an existing glucoregulatory model with glucagon and
exercise for simulation, and we use a simpler model for control. We test the AP
(NMPC and MLE) using in silico numerical simulations on 50 virtual people with
type 1 diabetes. The system is identified for each virtual person based on data
generated with the simulation model. The simulations show a mean of 89.3% time
in range (3.9-10 mmol/L) and no hypoglycemic events.</description><identifier>DOI: 10.48550/arxiv.2202.13938</identifier><language>eng</language><subject>Computer Science - Systems and Control ; Mathematics - Optimization and Control</subject><creationdate>2022-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2202.13938$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2202.13938$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Reenberg, Asbjørn Thode</creatorcontrib><creatorcontrib>Ritschel, Tobias K. S</creatorcontrib><creatorcontrib>Lindkvist, Emilie B</creatorcontrib><creatorcontrib>Laugesen, Christian</creatorcontrib><creatorcontrib>Svensson, Jannet</creatorcontrib><creatorcontrib>Ranjan, Ajenthen G</creatorcontrib><creatorcontrib>Nørgaard, Kirsten</creatorcontrib><creatorcontrib>Jørgensen, John Bagterp</creatorcontrib><title>Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas</title><description>In this work, we present a switching nonlinear model predictive control
(NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use
maximum likelihood estimation (MLE) to identify model parameters. A
dual-hormone AP consists of a continuous glucose monitor (CGM), a control
algorithm, an insulin pump, and a glucagon pump. The AP is designed with a
heuristic to switch between insulin and glucagon as well as state-dependent
constraints. We extend an existing glucoregulatory model with glucagon and
exercise for simulation, and we use a simpler model for control. We test the AP
(NMPC and MLE) using in silico numerical simulations on 50 virtual people with
type 1 diabetes. The system is identified for each virtual person based on data
generated with the simulation model. The simulations show a mean of 89.3% time
in range (3.9-10 mmol/L) and no hypoglycemic events.</description><subject>Computer Science - Systems and Control</subject><subject>Mathematics - Optimization and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tKxDAYhuFsXMjoBbgyN9CaQw_JcqingfEAzr78Tf5gIE0kjYNz92p19S0-eOEh5IqzulFty24gf_ljLQQTNZdaqnNinlMMPiJk-pQsBvqa0XpT_BHpkGLJKVCIlr6dloIz3VmMxTtvoPgUqUuZAr39hFC9pzyniHSb19_DTwqiyQjLBTlzEBa8_N8NOdzfHYbHav_ysBu2-wq6XlUSOUwd6xzIXopJt0ppZqGbtGxU37qmERy0Rc6tlqCMEwa0Zoo11vJOT3JDrv-yq3L8yH6GfBp_teOqld_6DFBX</recordid><startdate>20220228</startdate><enddate>20220228</enddate><creator>Reenberg, Asbjørn Thode</creator><creator>Ritschel, Tobias K. S</creator><creator>Lindkvist, Emilie B</creator><creator>Laugesen, Christian</creator><creator>Svensson, Jannet</creator><creator>Ranjan, Ajenthen G</creator><creator>Nørgaard, Kirsten</creator><creator>Jørgensen, John Bagterp</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20220228</creationdate><title>Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas</title><author>Reenberg, Asbjørn Thode ; Ritschel, Tobias K. S ; Lindkvist, Emilie B ; Laugesen, Christian ; Svensson, Jannet ; Ranjan, Ajenthen G ; Nørgaard, Kirsten ; Jørgensen, John Bagterp</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-3e1ab606fa3732b958890da6b934875f4421a9de11d93a8cf2ca990804dd169b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Systems and Control</topic><topic>Mathematics - Optimization and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Reenberg, Asbjørn Thode</creatorcontrib><creatorcontrib>Ritschel, Tobias K. S</creatorcontrib><creatorcontrib>Lindkvist, Emilie B</creatorcontrib><creatorcontrib>Laugesen, Christian</creatorcontrib><creatorcontrib>Svensson, Jannet</creatorcontrib><creatorcontrib>Ranjan, Ajenthen G</creatorcontrib><creatorcontrib>Nørgaard, Kirsten</creatorcontrib><creatorcontrib>Jørgensen, John Bagterp</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Reenberg, Asbjørn Thode</au><au>Ritschel, Tobias K. S</au><au>Lindkvist, Emilie B</au><au>Laugesen, Christian</au><au>Svensson, Jannet</au><au>Ranjan, Ajenthen G</au><au>Nørgaard, Kirsten</au><au>Jørgensen, John Bagterp</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas</atitle><date>2022-02-28</date><risdate>2022</risdate><abstract>In this work, we present a switching nonlinear model predictive control
(NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use
maximum likelihood estimation (MLE) to identify model parameters. A
dual-hormone AP consists of a continuous glucose monitor (CGM), a control
algorithm, an insulin pump, and a glucagon pump. The AP is designed with a
heuristic to switch between insulin and glucagon as well as state-dependent
constraints. We extend an existing glucoregulatory model with glucagon and
exercise for simulation, and we use a simpler model for control. We test the AP
(NMPC and MLE) using in silico numerical simulations on 50 virtual people with
type 1 diabetes. The system is identified for each virtual person based on data
generated with the simulation model. The simulations show a mean of 89.3% time
in range (3.9-10 mmol/L) and no hypoglycemic events.</abstract><doi>10.48550/arxiv.2202.13938</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Systems and Control Mathematics - Optimization and Control |
| title | Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas |
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