Linear parameter varying (LPV) based robust control of type-I diabetes driven for real patient data

Due to increasing prevalence of diabetes as well as increasing management costs, the artificial control of diabetes is a highly important task. Model-based design allows finding more effective solutions for the individual treatment of diabetic patients, but robustness is an important property that can be hardly guaranteed by the already developed individualized control algorithms. Modern robust control (known as H∞) theory represents an efficient possibility to solve robustness requirements in a general way based on exact mathematical formulation (Linear Matrix Inequalities) combined with knowledge-based expertise (through real patient data, uncertainty weighting functions can be formulated). When the difference between the nominal model and real patient dynamics is bounded and known, this approach becomes highly reliable. However, this requirement poses the greatest limitation since a model always represents an approximation of the complex physiological process. Consequently, the uncertainty formulation of the neglected dynamics becomes crucial as robust methods are very sensitive to them. In order to formulate them, large amount of real patient data and medical expertise is needed to cover the different life-style scenarios (especially the worst-case ones) that define the control space by the accumulated knowledge. On the other hand, H∞–based methods represent linear control techniques; hence their direct nonlinear application is important for a physiological process. The paper presents a roadmap of using modern robust control in diabetes focusing on nonlinear model-based interpretation: how the weighting functions should be selected based on (knowledge-based) medical expertise, the direct nonlinear applicability of the method taking additional advantage of the recently emerged Linear Parameter Varying (LPV) methodology, robust performance investigation and switching control possibilities. During the control characteristics discussion, the trade-off between the medical knowledge-based empiricism and exact control engineering formulation is introduced through different examples computed under MATLAB on real diabetic patient data

Preface to the Special Issue

T his Special Issue represents a best paper collection from the successful ly organized Women in Engineering workshop and Model - based Healthcare special sessions of the International Conference on Systems, Man , and Cybernetics (SMC) , the flagship conference of the IEEE SMC Society held in B udapest from 9 - 12 October 2016

Extension of the Bergman Minimal Model for the Glucose-insulin Interaction

In this paper, the extension of the Bergman model (minimal model) is proposed with an internal insulin control (IIC) part, representing the own insulin control of the human body. The model has been verified with clinical experiments, by oral glucose intake tests. Employing parameter estimation, for inverse problem solution technique (SOSI - `single output single input´) was developed using Chebysev shifted polynomials, and linear identification in time domain based on measured glucose and insulin concentration values was applied. The glucose and insulin input functions have been approximated and the model parameters of IIC were estimated. This extended Bergman model suits considerably better to the practical clinical situation, and it can improve the effectivity of the external control design for glucose-insulin process. The IIC part has been identified via dynamical neural network using the proposed SOSI technique. The symbolic and numerical computations were carried out with Mathematica 5.1, and with its application Neural Networks 2.0

Visual Monocular Obstacle Avoidance for Small Unmanned Vehicles

This paper presents and extensively evaluates a visual obstacle avoidance method using frames of a single camera, intended for application on small devices (ground or aerial robots or even smartphones). It is based on image region classification using so called relative focus maps, it does not require a priori training, and it is applicable in both indoor and outdoor environments, which we demonstrate through evaluations using both simulated and real data

Solving Robust Glucose-Insulin Control by Dixon Resultant Computations

We present a symbolic approach towards solving the Bergman three-state minimal patient model of glucose metabolism. Our work first translates the Bergman three-state minimal patient model into the modified control algebraic Riccati equation. Next, the modified control algebraic Ricatti equation is reduced to a system of polynomial equations, and an optimal (minimal) solution of these polynomials is computed using Dixon resultants. We demonstrate the use of our method by reporting on three case studies over glucose metabolism