Glucose-Insulin Dynamical Model for Type 2 Diabetic Patients

In this paper, a literature review is made for the current models of glucose-insulin dynamics of type 2 diabetes patients. Afterwards, a model is proposed by combining and modifying some of the available models in literature to take into account the effect of multiple glucose meals, multiple metformin doses, insulin injections, physical exercise, and stress on the glucose-insulin dynamics of T2D patients. The model is proposed as a candidate to be validated with real patients data in the future

An Online Stochastic Optimization Approach for Insulin Intensification in Type 2 Diabetes with Attention to Pseudo-Hypoglycemia

In this paper, we present a model free approach to calculate long-acting insulin doses for Type 2 Diabetic (T2D) subjects in order to bring their blood glucose (BG) concentration to be within a safe range. The proposed strategy tunes the parameters of a proposed control law by using a zeroth-order online stochastic optimization approach for a defined cost function. The strategy uses gradient estimates obtained by a Recursive Least Square (RLS) scheme in an adaptive moment estimation based approach named AdaBelief. Additionally, we show how the proposed strategy with a feedback rating measurement can accommodate for a phenomena known as relative hypoglycemia or pseudo-hypoglycemia (PHG) in which subjects experience hypoglycemia symptoms depending on how quick their BG concentration is lowered. The performance of the insulin calculation strategy is demonstrated and compared with current insulin calculation strategies using simulations with three different models.Comment: Preprint for a paper accepted and presented at CD

Learning-Based Predictive Control with Gaussian Processes: An Application to Urban Drainage Networks

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMany traditional control solutions in urban drainage networks suffer from unmodelled nonlinear effects such as rain and wastewater infiltrating the system. These effects are challenging and often too complex to capture through physical modelling without using a high number of flow sensors. In this article, we use level sensors and design a stochastic model predictive controller by combining nominal dynamics (hydraulics) with unknown nonlinearities (hydrology) modelled as Gaussian processes. The Gaussian process model provides residual uncertainties trained via the level measurements and captures the effect of the hydrologic load and the transport dynamics in the network. To show the practical effectiveness of the approach, we present the improvement of the closed-loop control performance on an experimental laboratory setup using real rain and wastewater flow data.Peer ReviewedPostprint (author's final draft