Regulation of blood glucose concentration in type 1 diabetics using single order sliding mode control combined with fuzzy on-line tunable gain, a simulation study

Abstract

Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin–blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

“…Other FL approaches have been tested using virtual patients or simulations [ 20 - 24 ]. For example, Miller et al reported a fuzzy controller combined with a learning algorithm that extracted initial patient profiles using open-loop data [ 23 ].…”

“…For example, Miller et al reported a fuzzy controller combined with a learning algorithm that extracted initial patient profiles using open-loop data [ 23 ]. Another example was provided by Dinani et al [ 24 ], who suggested combining fuzzy and sliding-mode controllers, with the goal of using feedback to govern the insulin delivery rate more aggressively.…”

Glucose Level Sensing Using Single Asymmetric Split Ring Resonator

“…According to a recent study from the World Health Organization and the international diabetes federation, there are around 463 million diabetes cases worldwide [ 1 , 2 ]. Generally, diabetes results when the pancreas is not able to produce enough insulin, or the latter is not effectively delivered to the cells.…”

“…More specifically, deficient production and inefficient use of insulin cause diabetes type 1 and type 2, respectively. When the blood sugar level is less than 70 mg/dL, the situation is called hypoglycemia [ 2 ]. Conversely, it is called hyperglycemia when the blood sugar level is more than 120 or 180 mg/dL when fasting or after meals, respectively [ 2 , 3 ].…”

Gain-Scheduled Observer-Based Finite-Time Control Algorithm for an Automated Closed-Loop Insulin Delivery System

“…Now, the nonlinear control techniques designed for the closed-loop AP system include; SMC approach presented in [24], provides a robust glucose regulation but the state unavailability and chattering phenomenon challenge its candidature to AP mechanism. Few synergistic strategies of fuzzy logics and SMC were focused in [25], [26]. However, these techniques required a long settling time and often degraded in uncertain situations.…”