This paper presents a novel hybrid control strategy for a specific class of nonlinear systems based on interval Type 3 fuzzy logic systems (IT3‐FLSs), and its effectiveness in the management of Type 1 diabetes is demonstrated. The main achievements of this work can be summarized as the following main points. First, an online system identification framework whose inputs are only from the measurable signals of the system, including glucose concentration and administered insulin, is introduced using a newly developed IT3‐FLS. Second, the system identification method is independent of the type of specific model or a predefined model and aims to estimate the measurable output of the system to predict the system behavior. Third, a novel hybrid controller consisting of a final control signal steady‐state controller, a proportional controller, a step‐forward predictive controller, and an adaptive compensator is developed. Fourth, through Lyapunov‐based stability analysis, adaptive learning rules are derived for both IT3‐FLS identifier and adaptive compensator, and finally the asymptotic stability of the closed‐loop system will be guaranteed. The proposed method is validated on a large range of virtual Type 1 diabetes patients modeled by the modified Bergman model (MBM) under significant uncertainties, noisy glucose measurements, and perturbations such as food intake. Comparative simulation results confirm the superior performance and robustness of the proposed hybrid strategy over existing insulin–glucose control approaches.
Ayadi et al. (Thu,) studied this question.