Independent Neuro-Fuzzy Control System

2008

In this paper a closed-loop control algorithm is developed for blood glucose regulation in type I diabetes mellitus patients. The control technique incorporates expert knowledge about treatment of disease by using Mamdani-type fuzzy logic controller to stabilize the blood glucose concentration in normoglycaemic level of 70 mg/dl. Controller performance is assessed in terms of its ability to reject the multiple meal disturbances resulting from food intake, on an averaged nonlinear patient model. Robustness of the controller is tested over a group of patients with model parameter varying considerably from the average model. In addition, proposed controller provides the possibility of more accurate control of blood glucose level in the patient in spite of uncertainty in model and measurement noise. Simulation results show the superiority of the proposed scheme in terms of robustness to uncertainty in comparison with other researches.

2013

Indonesian Journal of Electrical Engineering and Computer Science, 2022

Diabetes is one of the most common and critical diseases around the world, which need insulin injections to control the body's glucose rate. A robust back stepping (BS) controller design based on fuzzy system is introduced in this paper to control the glucose level with the presence of meal disturbance. The controller's design is based on Bergman's mathematical model. Different fuzzy controller structures are implemented (fuzzy PI, fuzzy PD, and fuzzy PID) controllers along with BS controller named as (BS-fuzzy PI, BS-fuzzy PD, BS-fuzzy PID) controllers. Simulation results using MATLAB/Simulink show efficiency and robustness of the proposed design in terms of controlling the insulin concentration level in blood under meal disturbance and retaining the glucose level to its Basal value.

2017

Biomedical instrumentation is an interesting and demanding area in which many fields are available to be discover and explore. Diabetes mellitus disease is one of the main segments of research area which is being addressed for the decades. One of the main functions of the pancreas is to regulate blood glucose concentration through releasing insulin enzyme. As a results of this deficiency blood glucose level concentrations (BGL) increased. Many researchers are working for diagnosing and treating diabetes mellitus disease. Mathematical models of blood glucose and insulin dynamics are used for this purpose. Through my work a simple mathematical model of the dynamics of glucose and insulin interaction in the blood system developed by Stolwijk and Hardy is used. By using Stolwijk and Hardy model, a closed-loop feedback system which regulates and manages the blood glucose-insulin will be designed, implemented and analyzed using Matlab/Simulink. Mamdani fuzzy logic algorithm controller (FLC) type is designed for exogenous insulin infusion pump. The control algorithm used expert knowledge about this disease treatment by using Mamdani-type to regulate the blood glucose level (BGL) for type I diabetes mellitus patient (TIDM). The used control strategy based on a continuous insulin infusion closed-loop control system to avoid traditional discrete diabetes mellitus treatment. Fuzzy-Genetic Algorithms controller was designed to deal with this plant through continuous insulin injection pump with choosing suitable membership functions as inputs and outputs for the proposed system in terms of keeping desired steady state plasma glucose level (0.81 mg/ml) against to exogenous glucose input. So a significant response of blood glucose-insulin regulation and management obtained.

International Journal of Industrial Electronics, Control and Optimization (IECO), 2025

In this research, an adaptive fuzzy controller is presented to regulate the blood glucose level of type 1 diabetic patients in the presence of input saturation. This controller along with an adaptive anti-windup compensator is considered to deal with the uncertainty of the Bergmann minimal nonlinear model parameters as well as the input saturation. Anti windup compensator is designed to prevent to saturation problems as hyperglycemia or hypoglycemia in regulating the blood glucose level of type 1 diabetes patients. The Bergman minimal model is used to mathematically model type 1 diabetes, depicting the dynamic behavior of the human body's blood glucose-insulin system. In the first step, the stability of the closed-loop system has been theoretically investigated and proved from the point of view of Lyapunov's theory. Next, to evaluate the effectiveness of the proposed method in regulating blood glucose levels, the proposed control system has been implemented in the presence of meal disturbances using the Simulink environment of MATLAB software. The implementation results show a lower control effort and less convergence time of the proposed method compared to the existing methods.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2018

In this paper, a Mamdani-type fuzzy controller is proposed as the controller part of an artificial pancreas. The controller is optimized with the artificial bee colony optimization algorithm. The glucose-insulin regulatory system, based on a nonlinear differential model in the presence of delay, is used both for virtual patient and healthy person data. The main target of the controller is to mimic a blood glucose concentration profile of the healthy person with exogenous insulin infusion. Simulations are performed to assess the control function in terms of tracking the blood glucose concentration profile of the healthy person and minimizing errors. To show robustness, a group of three tests are implemented. These tests include unusual glucose intake, sensor noise, and uncertainty in the clearance rate parameter. The simulation results demonstrate that the adopted method is more effective than similar studies in the literature.

Diabetes mellitus, an illness due the inefficiency of the pancreas in managing plasma glucose level, can result intensive side difficulties and death. Since the diabetes system is a nonlinear system the control of it associated with specific problems. For effective control, the use of fuzzy logic theory seems to be appropriate. In this study, Bergman model of type 1 diabetes is considered and is tried to design a fuzzy Proportional Integral (PI) controller base on Mamdani-type structure. In the design procedure, less linguistic rules are used so the proposed controller is less complicated than other existing fuzzy controllers. In order to evaluate the performance of the proposed controller, a control input and a disturbance input are derived from authoritative references and the system is tested on standard initial states for 10 h. The comparison with previous studies shows that the proposed controller reduces the blood glucose level in less time; however less insulin dosage in comparison with other optimal controllers is used.

Open Journal of Applied Sciences, 2013

This paper aims at the development of an approach integrating the fuzzy logic strategy for a glucose and insulin in diabetic human optimal control problem. To test the efficiency of this strategy, the author proposes a numerical comparison with the indirect method. The results are in good agreement with experimental data.

Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. The fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic cont rol strategy. The ability of fuzzy logic to handle imprecise and inconsistent real - world problems has made it suitable for a wide variety of applications. The present paper is concerned with modeling and control of nonlinear systems using fuzzy and neuro - f uzzy techniques. Design of controllers using conventional methods for nonlinear systems is difficult due to absence of a system atic theory behind it. In such cases, an approach based on the use of neural network for identifying the requirements o f the cont roller and the system from the input output data have been shown to be attractive. But identification using a neuro - fuzzy approach will help in reducing the arbitrariness in the choice of the type pf membership functions a nd the ranges of variables in the universe of discourse. This paper presents two methods based on fuzzy logic for the control of nonlinear systems, one using PID like fuzzy control and another using a neuro - fuzzy approach.

Diabetes is a serious disease during which the body's production and use of insulin is impaired, causing glucose concentration level to increase in the bloodstream. The blood glucose dynamics is described using the Bergman minimal model. In this paper, higher-order sliding mode control techniques, in specific prescribed convergence law, super-twisting control algorithm, is used to robustly stabilize the glucose concentration level of a diabetic patient in presence of the parameter variations and meal disturbance. Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical model in terms of linguistic rules. By advent of these methods, new techniques have appeared from which fuzzy logic been applied extensively in medical systems. This paper surveys the utilization of fuzzy logic control. Based on this method, fuzzy logic controller is designed to tackle a control problem of the resulting highly nonlinear plant. It was shown also that the proposed schemes can perform well in simulation experiments. Finally the obtained results from these two methods, are verified based on comparison via digital computer simulation by MATLAB.