Optimized nonlinear robust controller along with model-parameter estimation for blood glucose regulation in type-1...

Area of Science:

• Biomedical Engineering
• Control Systems Theory
• Endocrinology

Background:

• Glucose is vital for cellular energy and physiological processes.
• Precise blood glucose concentration (BGC) regulation is critical for health.
• Artificial pancreas systems use control algorithms for BGC management in diabetes.

Purpose of the Study:

• To develop and compare robust nonlinear controllers for stabilizing BGC in type 1 diabetic patients.
• To mitigate disturbances from meals and exercise using adaptive control strategies.
• To enhance health outcomes and quality of life for individuals with type 1 diabetes.

Main Methods:

• Proposed adaptive backstepping sliding mode control (AB-SMC) and adaptive backstepping integral super twisting sliding mode control (ABIST-SMC).
• Compared proposed controllers with backstepping sliding mode control (B-SMC) using the extended Bergman minimal model (EBMM).
• Employed adaptive parameter estimation, improved grey wolf optimization, and Lyapunov control theory for stability analysis.

Main Results:

• ABIST-SMC demonstrated the highest efficiency, achieving target BGC reduction in under 1.92 minutes.
• Proposed controllers showed robust performance and asymptotic stability via Lyapunov analysis.
• Simulations and hardware-in-loop experiments validated controller effectiveness.

Conclusions:

• The developed adaptive controllers offer effective BGC regulation for type 1 diabetes.
• ABIST-SMC presents a promising advancement in artificial pancreas control strategies.
• The study highlights the potential for improved diabetes management through advanced control techniques.