AI Article Synopsis
- - A new Model Predictive Control (MPC) method is presented for an Artificial Pancreas, designed to automatically regulate insulin delivery for people with type 1 diabetes.
- - This enhanced MPC approach targets safe use outside of clinical settings, maintaining blood-glucose levels within a time-dependent range and adhering to strict safety constraints.
- - The method uniquely incorporates asymmetric input costs to improve responses to high and low blood sugar levels, and its effectiveness has been validated through studies, including a clinical trial approved by the US FDA with 32 participants.
Article Abstract
A novel Model Predictive Control (MPC) law for an Artificial Pancreas (AP) to automatically deliver insulin to people with type 1 diabetes is proposed. The MPC law is an enhancement of the authors' zone-MPC approach that has successfully been trialled in-clinic, and targets the safe outpatient deployment of an AP. The MPC law controls blood-glucose levels to a diurnally time-dependent zone, and enforces diurnal, hard input constraints. The main algorithmic novelty is the use of asymmetric input costs in the MPC problem's objective function. This improves safety by facilitating the independent design of the controller's responses to hyperglycemia and hypoglycemia. The proposed controller performs predictive pump-suspension in the face of impending hypoglycemia, and subsequent predictive pump-resumption, based only on clinical needs and feedback. The proposed MPC strategy's benefits are demonstrated by studies as well as highlights from a US Food and Drug Administration approved clinical trial in which 32 subjects each completed two 25 hour closed-loop sessions employing the proposed MPC law.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5040369 | PMC |
| http://dx.doi.org/10.1016/j.automatica.2016.04.015 | DOI Listing |
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