PID Controller Tuning Using Multi-Objective Ant Colony Optimization for Blood Glucose Level of a Diabetic Patient

In this research, a nonlinear suboptimal controller was provided for type 1 diabetes patients' blood glucose level regulation based on a Proportional-Integral-Derivative (PID) controller with Ant Colony Optimization (ACO). The ACO approach is used to determine the ideal PID controller gain settings within a stability area. This study simulates three cases of a physiological model of glucose regulation in people with type 1 diabetes (No insulin treatment, fixed insulin injection, and Controlled blood glucose concentration using a PID controller) by using the Bergman Model in the context of meal-induced disturbances. In terms of dynamic performance, such as a reduction in maximum overshoot, settling time, and rising time, simulation findings suggest that the proposed technique for PID tuning using the ACO algorithm is more adaptable and cost-effective.