The development of new control models to represent more accurately the plasma glucose-insulin dynamics in T1DM is needed for efficient closed-loop algorithms. In this PhD thesis, we proposed a new nonlinear model of five time-continuous state equations with the aim to identify its parameters from easily available real patients' data (i.e. data from the insulin pump and the glucose monitoring system. Its design is based on two assumptions. Firstly, two successive remote compartments, one for insulin and one for glucose issued from the meal, are introduced to account for the distribution of the insulin and the glucose in the organism. Secondly, the insulin action in glucose disappearance is modeled through an original nonlinear form. The mathematical properties of this model have been studied and we proved that a unique, positive and bounded solution exists for a fixed initial condition. It is also shown that the model is locally accessible. In this way, it can so be used as a control model. We proved the structural identifiability of this model and proposed a new method based on the Kullback-Leiber divergence in view to test its practical identifiability. The parameters of the model were estimated from real patients' data. The obtained mean fit indicates a good approximation of the glucose metabolism of real patients. The predictions of the model approximate accurately the glycemia of the studied patients during few hours. Finally, the obtained results let us validate the relevance of this new model as a control model in view to be applied to closed-loop algorithms.
