Model Reference Adaptive Fuzzy Control of blood glucose level in patients with type 1 diabetes with input saturation

This study proposes a method that combines adaptive control, model references, and fuzzy logic with input saturation to control blood glucose levels in patients with type 1 diabetes. The proposed approach was applied to the nonlinear Bergman model, which accounts for meal disturbances and parameter uncertainties. In the adaptive fuzzy control of a reference model, a mathematical representation of the ideal system is used as a model reference, with the adaptive controller Trying to conform the real system's performance with this ideal model. Furthermore, this method includes an anti-windup compensator to address the system's nonlinear parameters and the Actuator performance degradation caused by input saturation. Furthermore, the controller design was analyzed to ensure the stability and convergence of the parameter error using the Lyapunov theory. To assess the effectiveness of the proposed method in tracking the desired blood glucose level and to compare it with other methods, simulations were conducted in the Simulink environment of MATLAB. The simulation results demonstrate that the proposed controller effectively reaches the desired value, accurately tracks the output during disturbances and parameter uncertainties, and converges faster than other methods.