An adaptive technique based blood glucose control in type‐1 diabetes mellitus patients

Abstract

This study proposes Grasshopper Optimization Algorithm (GOA) based type 1 diabetes mellitus system utilizing the nonlinear Bergman minimal model with proportional integral derivative (PID) controller. GOA is the optimization algorithm, which is utilized for selecting the optimized tuning parameters of the PID controller also solves the nonlinear system parameter identification problem. The novelty of the proposed study is to stabilize the glucose level in blood for type 1 diabetic patients by infusion of insulin in reduced time with optimal quantity. Without any intervention to the normal activities of patients, the supply of insulin injection and glucose monitoring is performed automatically for type 1 diabetic patients using this controller. In between the measured variable and set point, the difference is calculated by the PID controller to evaluate an error values. In realistic patient oriented conditions, the control performance evaluation, control optimization, and advanced patient modelling should be highly concentrated during the research/analysis on blood glucose control. Evaluation is performed to analyze control performances and implementation is done on Simulink/MATLAB environment. The performance analysis of the type 1 diabetes mellitus system with GOA technique is also discussed and to improve the control performance, to optimize the controller parameters. The simulation results have proved the substantial improvement in the performance of proposed algorithm with the better results achieved than the other conventional controllers such as PSO‐PID and EHO‐PID.

Design an Optimal Fractional Order PID Controller Based on New Algorithms and a Fuzzy Logic Controller to Regulate Type 1 Diabetes Patients

“…Other than the control parameters Kp, Ki, and Kd of a regular PID controller there are λ and μ, the fractional powers of the integral and derivative parts respectively. It can be defined mathematically using the following differential equation [31,32]:…”

“…IWO is a new evolutionary computational algorithm that relies on the natural behavior of weeds in searching for a suitable place for growth and reproduction by simulating the reproductive and growth behaviors of weeds in nature as the IWO algorithm is a random numerical search algorithm that IWO searches for the optimal solution to the problem in the solution space. In general, its most important steps in the calculation are based on four steps: initialization, cloning, spatial dispersion, and selection, which are as follows [30,31].…”

“…After that, all Wj grass in the community reproduces seeds, and the seeds grow into multiple weeds through spatial dispersal. The amount (Nsj) of seeds produced by Wj is calculated using the following equation [30,31]:…”

“…By repeating Itermax times, weeds with the highest fitness value are the perfect solution to the problem. The formula is as follows [30,31]:…”

“…That is why our contribution to this research was on the application of a FOPID controller for good blood glucose control, closed loop control, and good response determination respectively. By finding a better solution by applying several new meta-heuristic algorithms including the Particle Swarm Optimization (PSO) [28,29], The bat optimization algorithm (BA), Invasive Weed Optimization (IWO) [30,31], Ant Colony Optimization (ACO) [32]. and technology Genetic algorithm (GA) [33].…”