Adaptive Zone MPC for Artificial Pancreas Glucose Control
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Solution Overview
Problem
Current glucose control systems for diabetes management, particularly artificial pancreas systems, face challenges in effectively regulating blood glucose levels to prevent hyperglycemia and hypoglycemia, requiring improved smart control algorithms for continuous and automated insulin delivery.
Innovation Solution
A closed-loop system incorporating a glucose sensor, insulin delivery device, and a controller using a zone model predictive control (MPC) algorithm that adapts control parameters based on predicted glucose concentrations and velocity, enabling real-time insulin dosage adjustments to maintain safe glucose levels without requiring online insulin or glucose data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional zone model predictive control algorithm is used, then the system structure is simple, but the glucose control performance is insufficient and cannot effectively prevent hyperglycemia and hypoglycemia
Solution Approach 1:
The patent implements dynamic adaptation of control parameters (penalty factors and control horizons) based on real-time glucose velocity predictions. The controller adjusts these parameters dynamically according to the predicted glucose change rate, transforming a static control algorithm into a dynamic one that adapts to changing physiological conditions, thereby improving control reliability without requiring complex online insulin or glucose data
Solution Approach 2:
The patent changes key control parameters (penalty factors and control horizons) based on predicted glucose velocity. By modifying these parameters dynamically according to the predicted rate of glucose change, the system achieves better glucose control performance while maintaining a relatively simple algorithm structure that does not require online insulin or glucose data
2Reliability
If control parameters are adapted based on online insulin or glucose data, then the control performance is improved, but the system complexity and computational burden increase
Solution Approach 1:
The patent extracts only the necessary information (predicted glucose velocity from the prediction model) needed for parameter adaptation, eliminating the need for complex online insulin or glucose data processing. By taking out only the essential predictive information, the system achieves improved control performance while keeping the computational burden manageable
Solution Approach 2:
The patent introduces predicted glucose velocity as an intermediary variable that bridges the prediction model and control parameter adaptation. This intermediary allows the system to adapt control parameters based on predicted glucose trends without requiring direct online insulin or glucose data, thereby reducing computational complexity while maintaining improved control performance
3Adaptability or versatility
If a fixed control parameter strategy is used, then the system is easy to implement, but it cannot adapt to different glucose states and glucose velocity conditions
Solution Approach 1:
The patent makes the control parameters dynamic by linking them to predicted glucose velocity. The controller automatically adapts to different glucose states and velocity conditions through this dynamic mechanism, achieving high adaptability while maintaining ease of implementation through a systematic approach
Solution Approach 2:
The patent systematically changes control parameters based on predicted glucose velocity to adapt to different glucose states. This parameter change strategy provides adaptability to various conditions while maintaining implementation ease through a structured methodology
4Speed
If the control system responds aggressively to glucose changes, then hyperglycemia is corrected quickly, but the risk of inducing hypoglycemia increases
Solution Approach 1:
The patent implements dynamic adjustment of control aggressiveness based on predicted glucose velocity. When glucose is rising rapidly, the controller becomes more aggressive to correct hyperglycemia quickly; when glucose is falling or stable, the controller reduces aggressiveness to prevent hypoglycemia, thus achieving both fast correction and safety
Solution Approach 2:
The patent applies preliminary anti-action by using predicted glucose velocity to anticipate future glucose trends. The controller pre-adjusts parameters to prevent excessive insulin administration that could lead to hypoglycemia, while still enabling quick correction of hyperglycemia when needed
Data Source
AI summary
A system for the delivery of insulin to a patient is provided. The systems and methods disclose include an insulin delivery device configured to deliver insulin to a patient in response to control signals. The system also includes a controller programmed to receive the sensor glucose measurement signal from the glucose sensor. The sensor glucose measurement signal received indicates a concentration of the real time glucose concentration in a bloodstream. The controller is further configured to enact an impeding glycemia protocol based on a zone model predictive control (MPC) algorithm in response to the real time glucose concentration. The impeding glycemia protocol includes in determining a relationship between predicted glucose concentrations, a rate of change of the predicted glucose concentrations, and a set of control parameters that determine insulin doses above and below a patient-specific basal rate.