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• W2735733765 abstract "An essential component in the design of a wearable or implantable artificial pancreas is a low-power, embedded control algorithm capable of tight glucose regulation in people with Type 1 Diabetes Mellitus (T1DM). In this paper, physiological insights into glucose management are leveraged to formulate a safety-constrained, event-triggered model predictive control (MPC) algorithm customized to reduce the number of controller updates, leading to reduced processor runtime and lowered energy expenditure. The proposed event-triggered MPC is deployed on a wearable (65 mm × 30 mm × 5 mm) embedded prototyping platform running a single core 1 GHz 32-bit ARM11 processor. The efficiency of the proposed method is verified in a hardware-in-the-loop simulation study with ten subjects simulated in-silico using the United States Food and Drug Administration (US FDA) accepted UVA/Padova Metabolic Simulator. The controller is challenged with large, unannounced meals and sudden hypoglycemia. Empirical studies reveal that the proposed event-triggered MPC remains idle for 50% of the time in closed-loop, while maintaining 32 (out of 53) hours in the clinically accepted safe glucose region (70–180 mg/dL), compared to 35.5 hours for its time-triggered counterpart, with reduced time in hypoglycemia. Thus, the proposed strategy significantly reduces the number of controller updates required, without compromising safety in glycemic regulation." @default.
• W2735733765 created "2017-07-21" @default.
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• W2735733765 date "2017-05-01" @default.
• W2735733765 modified "2023-09-23" @default.
• W2735733765 title "Reducing controller updates via event-triggered model predictive control in an embedded artificial pancreas" @default.
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• W2735733765 doi "https://doi.org/10.23919/acc.2017.7962943" @default.
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