Adaptive fuzzy control-based optimization models for real-time insulin delivery in diabetes
Supriya Maheshwarisupmaheshwari@gmail.comDepartment of MathematicsNoida Institute of Engineering and TechnologyGreater Noida, Uttar Pradesh, 201306, IndiaView full profile → , Khursheed Alamkhursheed.alam@sharda.ac.inDepartment of Mathematics & Data ScienceThe A. H. Siddiqi Centre for Advanced Research in Applied Mathematics & Physics (CARAMP)Sharda UniversityGreater Noida, Uttar Pradesh, 201310, IndiaView full profile → , *Masood AlamCorresponding authormalam@squ.edu.omDepartment of Mathematics and Information TechnologyCentre for Preparatory Studies (CPS)Sultan Qaboos UniversityMuscat, OmanView full profile → , A. K. Singhaksinghrbs@yahoo.comDepartment of Applied Sciences (Mathematics)Raja Balwant Singh Engineering Technical CampusBichpuri, Agra, Uttar Pradesh, 283105, IndiaView full profile → , Surekha Manohar Jadhavsurekha.kale@pravara.inDepartment of Bachelor of Computer ApplicationLoknete Dr. Balasaheb Vikhe Patil (Padmbhushan Awardee), Pravara Rural Education Society’sWomen’s College of Home Science and BCALoni, Pune, Maharashtra, 413713, IndiaView full profile → , Firdaus Mohamad Hamzahkagba2006@gmail.comDepartment of MathematicsCentre for Defence Foundation StudiesUniversiti Pertahanan Nasional MalaysiaKem Sungai Besi, Kuala Lumpur, 57000, MalaysiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jan 2026
- Published Online:
- 30 Sep 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2421
- Pages:
- 3293–3308
Abstract
An adaptive fuzzy control model is developed as an optimization-based real-time insulin delivery system for managing diabetes. The fuzzy controller uses 3 input values (blood glucose levels, rate of change of glucose, and amount of food consumed) to adjust the rate at which insulin is infused based on how close the glucose level is to the desired range. In comparison to traditional Proportional Integral Derivative (PID) type controllers, that have difficulty dealing with both time delay and the non-linear relationship of glucose and insulin, the adaptive fuzzy controller performs optimization by adjusting linguistic rules and tuning membership functions in real time to produce less erratic and more stable responses. The simulation demonstrates that the fuzzy controller is capable of reducing glucose excursions, preventing hypoglycemia, and returning to target more quickly than the PID controller after a meal. Comparisons of the fuzzy controller to the PID controller show the potential of this optimization-based technology as part of a closed loop artificial pancreas.
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References
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