György Eigner

Linear matrix inequality-based robust controller design for type-1 diabetes model

Abstract This paper investigates the capabilities of a sophisticated robust nonlinear controller designed directly for a widely known and used high-order nonlinear type 1 diabetes (T1DM) model to lessen the dependency from patient compliance and to answer practical requirements such as avoiding hypoglycaemia. The resulting controller can perform adequately in nominal conditions, but expected to keep this performance even in extreme situations, e.g. high carbohydrate intake, rejecting hypoglycaemic episodes.

LPV-based quality interpretations on modeling and control of diabetes

In this study we introduce different novel interpretations in the case of Linear Parameter Varying (LPV) methodology, which are directly usable in modeling and control design in diabetes research. These novel interpretations are based on the parameter vectors of the LPV parameter space. The theoretical solutions are demonstrated on a simple, known Type 1 Diabetes Model used in intensive care.

The significance of LPV modeling of a widely used T1DM model

The paper investigates the specificity of Linear Parameter Varying (LPV) modeling and robust controller design on a widely used Type 1 Diabetes Mellitus model. LPV systems can be seen as an extension of linear time invariant systems, which allows us to extend some powerful control methodologies to the highly nonlinear and uncertain models of the human metabolism. Different LPV models are proposed with their own advantages and disadvantages. The possible choices are separately analyzed for both controller and observer design perspective.

Teacher's Kit: Development, Usability, and Communities of Modular Robotic Kits for Classroom Education

Robotics is becoming a mainstream phenomenon, entering all areas of our lives. In addition to cutting-edge research and development, robotics is becoming equally important in the classroom and home education. Numerous educational kits have appeared on the market recently, ranging from simple toolboxes and toys to complex, configurable R&D sets. Their value in formal teaching lies in modularity and the applicability of the associated curriculum. Some kits have already attracted major crowds of users, forming strong communities. The aim of this article is to review the currently available educational robotics kits along with their possible usability in formal education, focusing the analysis on system capabilities, modularity, and teaching materials available. The summary of these teaching aids should ease the decisions of robotics experts and instructors when choosing their tools for teaching and demonstration.

Model-based optimal therapy for high-impact diseases

The current paper represents a continuation of the conference papers presented at the INES 2011 and 2012 conferences. A brief summary of the newest results in the field of physiological modeling and control are presented focusing on high public health impact diseases: diabetes, tumor and obesity. In the case of diabetes control, preliminary model-free results of the developed robust control framework is presented, while in the case of tumor control we have started to create a new tumor growth model based on clinical experiments. In the case of biostatistical investigation of obesity, new aspects of relationship between overweight/obesity and laboratory results was examined.

Application of Robust Fixed Point Control in Case of T1DM

Adaptive, model-free control of Type 1 Diabetes Mellitus (T1DM) is a lack in the field of diabetes control, since, most of the applied control strategies are model-based ones. The main problem is that difficult to formulate exact mathematical models to replicate the physiological processes, not just because of their behavior, rather then these processes are changing patient-by-patient. Furthermore, the developed models so far, are highly non-linear and difficult to manage. A possible adaptive control solution can be the recently developed Robust Fixed Point Transformation (RFPT)-based control design method, which can provide control action, based on the observations about the actual output of a controlled system. In this paper we show a survey, how can be used this novel technique related with a known, highorder glucose-insulin model, to investigate the usability according to diabetes control.

Comparison of sigma-point filters for state estimation of diabetes models

In physiological control there is a need to estimate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma-point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model.

Receding Horizon Control of Type 1 Diabetes Mellitus by Using Nonlinear Programming

Receding Horizon Controllers are one of the mostly used advanced control solutions in the industry. By utilizing their possibilities we are able to predict the possible future behavior of our system; moreover, we are able to intervene in its operation as well. In this paper we have investigated the possibilities of the design of a Receding Horizon Controller by using Nonlinear Programming. We have applied the developed solution in order to control Type 1 Diabetes Mellitus. The nonlinear optimization task was solved by the Generalized Reduced Gradient method. In order to investigate the performance of our solution two scenarios were examined. In the first scenario, we applied “soft” disturbance—namely, smaller amount of external carbohydrate—in order to be sure that the proposed method operates well and the solution that appeared through optimization is acceptable. In the second scenario, we have used “unfavorable” disturbance signal—a highly oscillating external excitation with cyclic peaks. We have found that the performance of the realized controller was satisfactory and it was able to keep the blood glucose level in the desired healthy range—by considering the restrictions for the usable control action.

Adaptive control solution for T1DM control

The “Type 1 Diabetes Mellitus (T1DM)” is a dangerous illness that concerns yearly increasing population. The control of the glucose level in the human body is a widely investigated subject area that also has serious technical difficulties as the lack of reliable system model for each individual patient, the limitations regarding the observability of the complete internal state of the patient (at least in the view of the system model). On this reason the “Model Predictive Control (MPC)” needs either robust or adaptive completion in this field of application. In the lack of observable data the traditional state estimators may have only limited relevance. The “Robust Fixed Point Transformation (RFPT)” based method was elaborated for the design of adaptive controllers typically for such situations. It does not need any sophisticated system model, it can work on the basis of observations that concern only the controlled quantity without the need of complete state estimation. In the present paper the use of the RFPT-based adaptive controller is reported in simulation investigations in which the validity of Bergmans “Minimal Model” is assumed. Promising simulation results are presented.

Novel error interpretation in case of linear parameter varying systems

The purpose of this paper is to introduce novel error definitions in the case of Linear Parameter Varying (LPV) systems, which can be used as quality criteria in LPV related modeling and control. The current approach relies on the convexity of the LPV polytopic system by defining the difference in the abstract parameter space. The theoretical approach is demonstrated on a well-known glucose-insulin model used in intensive care.