Abstract
The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions.
Highlights
Licensee MDPI, Basel, Switzerland.Through the development of electronic devices and sensors, modern control systems are extensively used in medical applications
We proposed a new fuzzy approach for glucose level control in type-I diabetes
The dynamics of glucose changes were assumed to be uncertain and an adaptive generalized type-2 (GT2)-fuzzy-logic systems (FLSs) was presented for online estimation of the glucose–insulin metabolism
Summary
[20], the blood glucose situation was analyzed by FLSs, and the suitable insulin level was determined by the FLS model. [21], an artificial pancreas was designed by the use of FLSs, and better performance of the FLS model was shown in comparison with the Bergman model. [25], the IT2-FLSs were used for the estimation of glucose–insulin dynamics, and the controller was designed on the basis of a fuzzy model. [29], the changes of model parameters were taken into account, and a IT2-FLS-based controller was designed and the better performance of IT2-FLSs was validated. The suggested GT2-FLS is optimized at every glucose measurement considering the dynamic stability criteria The challenging conditions, such as dynamic perturbation by noisy signals, meal effect, and estimation errors are taken into account, and an adaptive fuzzy compensator is designed
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