Performance Comparison between Type-1 and Type-2 Fuzzy Logic Control Applied to Insulin Pump Injection in Real Time for Patients with Diabetes

Abstract

Nowadays, type 1 diabetes is unfortunately one of the most common diseases, and people tend to develop it due to external factors or by hereditary factors. If is not treated, this disease can generate serious consequences to people’s health, such as heart disease, neuropathy, pregnancy complications, eye damage, etc. Stress can also affect the condition of patients with diabetes, and our motivation in this work is to help manage the health of people with type 1 diabetes. The contribution of this paper is in presenting the implementation of type-1 and type-2 fuzzy controllers to control the insulin dose to be applied in people with type 1 diabetes in real time and in stressful situations. First, a diagram for the insulin control is presented; second, type-1 and type-2 fuzzy controllers are designed and tested on the insulin pump in real time over a 24 h period covering one day; then, a comparative analysis of the performance of these two controllers using a statistical test is presented with the aim of maintaining a stable health condition of people through an optimal insulin supply. In the model for the insulin control, perturbations (noise/stress levels) were added to find if our proposed fuzzy controller has good insulin control in situations that could generate disturbances in the patient, and the results found were significant; in most of the tests carried out, the type-2 controller proved to be more stable and efficient; more information can be found in the discussion section.

Designing a Fuzzy Sliding Mode Controller for 2-DOF Lower Limb Exoskeleton Rehabilitation Robot

“…The Mamdani-style fuzzy inference process (the most commonly used fuzzy inference technique) is performed in four steps: fuzzification, rule evaluation, aggregation of the rule outputs,and defuzzification [27]. For the membership functions of the fuzzy logic controller I used both triangular and membership functions [28]. The output is produced by fuzzy sets and fuzzy logic operations by evaluating all the rules.…”

Modified Flower Pollination Optimization Based Design of Interval Type-2 Fuzzy PID Controller for Rotary Inverted Pendulum System

“…In recent decades, contemporary control techniques such as nonlinear, variable structure, adaptive, and optimal methods are employed, but they tend to be complicated and not easily implemented [3,4]. As a result, the control engineering community has become interested in using Type 2 fuzzy logic controllers (T2FLC) for nonlinear systems control, which has shown promising results in various successful applications [5,6]. It is hoped that these strategies will be generalized to other difficult control problems in the future.…”