Towards Safe and Robust Closed-Loop Artificial Pancreas Using Improved PID-Based Control Strategies
Bibliographic record
Abstract
Artificial pancreas enhances the life experience for diabetic patients by allowing them to live normally with their glucose levels controlled automatically with minimal or no intervention. For closed-loop glucose controllers to be approved for clinical practice, they have to prove safety under all potential scenarios. One of the biggest challenges of closed-loop glucose control is to handle the distortion caused by meal intake. This challenge becomes more problematic when taking into account the imperfections and limitations of glucose sensors. In this article, we propose new Proportional-Integral-Derivative (PID)-based control strategies for robust glucose control under varying meal conditions. The proposed approaches aim at counteracting the challenges imposed by the large delays incurred in glucose sensing and insulin action. Statistical model checking was utilized to analyze the performance figures and safety properties as compared with existing closed-loop techniques. The results have shown that one of the proposed approaches provide substantial enhancements towards safe and robust glucose control especially under sensor noise. Where, under a typical relative meal size between 75 and 125 (g/100Kg), the proposed approach can satisfy hypoglycemia safety property for 90% of the patients compared to lower than 50% of the patients for the other investigated techniques. These enhancements can be achieved without additional personalized tuning beyond the standard PID control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".