Adaptive MPC Algorithm for Closed-Loop Glucose Control
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Solution Overview
Problem
People with type 1 diabetes face significant burdens in monitoring and regulating their blood glucose levels, often leading to debilitating complications from both high and low glucose levels due to the demanding regimen of frequent monitoring and insulin administration.
Innovation Solution
A closed-loop control system utilizing model predictive control (MPC) with an adaptive algorithm that automatically delivers insulin based on real-time glucose measurements, incorporating a counter-regulatory agent like glucagon to minimize insulin overdosing and optimize glucose regulation, while also considering subcutaneous insulin accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If frequent manual monitoring and insulin administration is performed, then blood glucose control can be maintained, but patient burden and quality of life deteriorate
Solution Approach 1:
The system enables self-service automation where the closed-loop control system automatically monitors blood glucose levels and administers insulin without requiring manual patient intervention. The glucose sensor continuously measures glucose levels, and the controller automatically adjusts insulin delivery based on these measurements, freeing the patient from the burden of frequent manual monitoring and injection while maintaining reliable glycemic control.
Solution Approach 2:
The patent replaces the manual mechanical system of patient self-monitoring and self-injection with an automated electronic control system. The mechanical action of manual blood sampling and insulin injection is substituted by an electronic glucose sensor and an automated insulin pump controlled by a closed-loop algorithm, significantly reducing patient burden while maintaining control reliability.
2Ease of operation
If automated insulin delivery is implemented, then patient burden is reduced, but risk of insulin overdosing increases
Solution Approach 1:
The system implements continuous feedback through a closed-loop control architecture where glucose levels are continuously measured by a sensor and fed back to the controller. The controller uses this real-time feedback to dynamically adjust insulin delivery, preventing both overdosing and underdosing. The feedback mechanism allows the system to respond to changing glucose levels and adapt insulin dosing accordingly, maintaining dosing accuracy while automating the process.
Solution Approach 2:
The control system employs dynamic algorithms that continuously adapt insulin delivery based on real-time glucose measurements and predicted future glucose levels. The system dynamically adjusts the insulin rate rather than using fixed dosing schedules, allowing it to respond to varying physiological conditions, meal intake, and activity levels, thereby maintaining dosing accuracy while reducing patient burden.
3Device complexity
If traditional insulin control methods are used, then simplicity is maintained, but glycemic control quality and prevention of complications deteriorate
Solution Approach 1:
The system performs preliminary action by predicting future glucose levels based on current glucose measurements and physiological models. The controller uses these predictions to proactively adjust insulin delivery before hyperglycemia or hypoglycemia occurs, preventing complications before they arise. This predictive capability allows the system to maintain high glycemic control quality while managing complexity through automated decision-making.
Data Source
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AI summary
An augmented, adaptive algorithm utilizing model predictive control (MPC) is developed for closed-loop glucose control in type 1 diabetes. A linear empirical input-output subject model is used with an MPC algorithm to regulate blood glucose online, where the subject model is recursively adapted, and the control signal for delivery of insulin and a counter-regulatory agent such as glucagon is based solely on online glucose concentration measurements. The MPC signal is synthesized by optimizing an augmented objective function that minimizes local insulin accumulation in the subcutaneous depot and control signal aggressiveness, while simultaneously regulating glucose concentration to a preset reference set point. The mathematical formulation governing the subcutaneous accumulation of administered insulin is derived based on nominal temporal values pertaining to the pharmacokinetics (timecourse of activity) of insulin in human, in terms of its absorption rate, peak absorption time, and overall time of action.