Algorithms for a Closed-Loop Artificial Pancreas: The Case for Proportional-Integral-Derivative Control

Steil, Garry M.

doi:10.1177/193229681300700623

Cited by 105 publications

(54 citation statements)

References 33 publications

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“…PID controllers operate on the difference between the measured and the desired glucose levels and calculate a control signal (insulin infusion) that is then applied on the system (patient). The relatively simple implementation and the argument that the PID algorithm is expected to emulate and replace the biological pancreas action well [54,55] have established PID controllers as a significant candidate for the AP system [56,57]. The wealth of clinical experience behind the decision-making for glucose regulation has, on the other hand, motivated the consideration of fuzzy logic control as a potential candidate [58].…”

Section: Control Algorithms For the Artificial Pancreasmentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

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Section: Control Algorithms For the Artificial Pancreasmentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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“…To date, the two main methods used to develop glucose control systems have been proportional integral derivative (PID) [10] and model predictive control (MPC) [11]. However, we have recently shown in a large scale simulation study that an artificial intelligence (AI) based controller may be capable of achieving results that are superior to those of both PID and MPC controllers [12].…”

Section: Is Closed Loop Glucose Control For Icumentioning

confidence: 99%

Is Closed Loop Glucose Control for ICU Patients Just Around the Corner?

DeJournett¹,

DeJournett²

2017

J Intensive Crit Care

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Effective glucose control in the Intensive Care Unit (ICU) setting has the potential to lower mortality rates [1], shorten length of stay [2] and decrease overall cost of care [3]. Yet the goal of achieving this control remains elusive due to the limitations of our current open loop methods [4] that still require manual testing of glucose values, entry of the measured value into local or web based glucose control software, and manual adjustment of the intravenous pumps infusing insulin into the ICU patient. In order to improve overall glucose control, ICU care givers will need to be empowered with a closed loop glucose control system.The three main components of a closed loop glucose control system are a glucose sensor(s), control algorithm, and intravenous pump(s). Current intravenous pumps are accurate and reliable enough for a closed loop system, so the two components preventing completion of the system are the glucose sensor(s) and controller. An accurate and reliable glucose sensor array is a must, as trying to control a system without real time knowledge of mission critical sensor data will invariably lead to unacceptable outcomes, as has been seen in the aerospace industry [5]. In fact, the aerospace industry provides an excellent example of how to properly engineer a safe and effective control system, as they routinely build in redundancy of mission critical system components to improve reliability and will also use different methods of measurement to improve overall accuracy.Studies on type I diabetics have shown that a multi-sensor array improves overall accuracy of the system [6]. The following equation can be used as a guide to determine the number of sensors N, needed given a known sensor failure rate p and a desired uptime rate q, where p, q ∈ [0,1]:As can be seen from Table 1 if the closed loop system requirement is to have a complete sensor failure rate limited to less than 10 min over a typical 96 h ICU length of stay, which is a greater than 99.8% uptime rate, and the known sensor failure rate is 1%, then the system requirement is to have two independent glucose sensors in place.Current CE marked blood based glucose sensors designed for use in the ICU setting have already been shown to be both highly accurate and reliable, with an uptime rate that exceeds 99% [7]. In addition, the next generation interstitial continuous glucose monitoring (CGM) system that Dexcom is developing with assistance from Google will not be affected by acetaminophen, making it a potential excellent candidate as the second sensor in a glucose array [8]. The ability to avoid interference from acetaminophen is important as this medication is known to affect the accuracy of current interstitial CGM systems, yet is ubiquitously used in the hospital setting. As sensor reliability is far more important than accuracy when it comes to closed loop glucose control [9], the improved uptime rate of a two sensor array will more than offset the decreased accuracy that will occur by averaging simultaneous blood and interstitial glucos...

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“…Subcutaneous rapid-acting insulin absorption kinetics from Shimoda et al [5] is given by dx 1 (t) dt = −k 21 x 1 (t) + u s (t) (S10)…”

Section: Mathematical Model Of Glucose-insulin Metabolismmentioning

confidence: 99%

“…Proportional-integral-derivative control [5] and model predictive control (MPC) [6] are the most widely utilized algorithms for BG control purposes, with different approaches being constantly proposed, including fully closed-loop control algorithms with automatic meal detection based on changes in BG levels [7] and semi closed-loop systems with improved postprandial BG control performance at the expense of additional input from the patient for an intended meal (meal announcement) [8]. Others favor a xed pre-meal bolus of 2 IU [9] or over-bolus [10] along with closedloop BG control, while a recent intraperitoneal insulin infusion [11] offers an interesting alternative to subcutaneous route due to faster insulin responses.…”

Section: Introductionmentioning

confidence: 99%

<i>In Silico</i> Blood Glucose Control for Type 1 Diabetes with Meal Announcement Using Carbohydrate Intake and Glycemic Index

Noguchi

Hashimoto

Furutani

2016

ABE

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An increasing number of closed-loop blood glucose (BG) control algorithms have been developed in recent years with the arti cial pancreas as the ultimate goal, although tight postprandial BG control remains an elusive goal. In this report, the authors propose a novel semi closed-loop BG control algorithm with meal announcement, which involves computation of the optimal continuous subcutaneous insulin infusion for a speci c meal 60 min prior to mealtime. It utilizes a mathematical model of glucose-insulin metabolism to predict the impact of carbohydrates on postprandial BG levels based on carbohydrate intake and glycemic index (GI) value. The optimal pre-meal insulin is infused until mealtime, after which the control algorithm switches to model predictive control (MPC) to stabilize postprandial glycemia at the target value of 100 mg/dL (5.55 mmol/L). In silico results for four representative foods with GI values spanning a wide range show that in the case of exact patient-model match with precise information of carbohydrate composition and mealtime, postprandial BG levels can be maintained between 86-134 mg/dL (4.78-7.44 mmol/L) and 86-152 mg/dL (4.78-8.44 mmol/L) for 50 g and 100 g of carbohydrates, respectively. With consideration of intra-patient variability and meal-related uncertainties regarding the estimated carbohydrate amount and start of meal consumption, the BG control range is 75-159 mg/dL (4.17-8.83 mmol/L) with no critical hypoglycemic episodes.

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Algorithms for a Closed-Loop Artificial Pancreas: The Case for Proportional-Integral-Derivative Control

Cited by 105 publications

References 33 publications

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Is Closed Loop Glucose Control for ICU Patients Just Around the Corner?

<i>In Silico</i> Blood Glucose Control for Type 1 Diabetes with Meal Announcement Using Carbohydrate Intake and Glycemic Index

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