Marián Tárník

Adaptive Control of Time-Delayed Systems with Application for Control of Glucose Concentration in Type 1 Diabetic Patients

Abstract An adaptive controller for glucose control in Type 1 Diabetes Mellitus (T1DM) subject is presented in this paper. The proposed control model of T1DM subject involves a known input time-delay, due to the modeling of a subcutaneous tissues, and a disturbance submodel, where a meal ingestion acts as a measured disturbance. A main MRAC based part of controller for time-delayed systems is supplemented with a heuristic based adaptive disturbance rejection. The controller is verified by means of numerical simulations using an own implementation of T1DM simulator reported in literature.

Bihormonal model predictive control of blood glucose in people with type 1 diabetes

In this paper we present a bihormonal control system that controls blood glucose in people with type 1 diabetes (T1D). We use insulin together with glucagon to mitigate the negative effects of hyper- and hypoglycemia. The system consists of a Kalman filter, a micro-bolus insulin and glucagon infusion MPC, a mealtime bolus calculator and a CGM providing feedback to the controller. The controller employs a patient data-based prediction model with ARMAX structure. We test the controller using a bihormonal model with time-varying parameters for 3 subjects and compare its performance to a system with an identical insulin MPC, but a glucagon PD controller. The key contribution of the bihormonal MPC is the efficiency of glucagon use. We consider scenarios where the meals are estimated correctly or overestimated and where the insulin sensitivity increases. Both solutions provide tight glucose control. According to the simulations, the bihormonal MPC requires on average 30% less glucagon than the system with a PD controller.

Model Reference Adaptive Control of Permanent Magnet Synchronous Motor

Model Reference Adaptive Control of Permanent Magnet Synchronous Motor In this paper the classical theory of the direct Model Reference Adaptive Control is used to develop a control algorithm for Permanent Magnet Synchronous Motor (PMSM). A PMSM model widely used in electric drives community is considered as base for control system development. Conventionally used controllers are replaced by adaptive ones. The resulting control system adapts to changes in any of PMSM parameters.

The contribution of glucagon in an Artificial Pancreas for people with type 1 diabetes

The risk of hypoglycemia is one of the main concerns in treatment of type 1 diabetes (T1D). In this paper we present a head-to-head comparison of a currently used insulin-only controller and a prospective bihormonal controller for blood glucose in people with T1D. The bihormonal strategy uses insulin to treat hyperglycemia as well as glucagon to ensure fast recovery from hypoglycemic episodes. Two separate model predictive controllers (MPC) based on patient-specific models handle insulin and glucagon infusion. In addition, the control algorithm consists of a Kalman filter and a meal time insulin bolus calculator. The feedback is obtained from a continuous glucose monitor (CGM). We implement a bihormonal simulation model with time-varying parameters available for 3 subjects to compare the strategies. We consider a protocol with 3 events - a correct mealtime insulin bolus, a missed bolus and a bolus overestimated by 60%. During normal operation both strategies provide similar results. The contribution of glucagon becomes evident after administration of the overestimated insulin bolus. In a 10h period following an overbolused meal, the bihormonal strategy reduces time spent in hypoglycemia in the most severe case by almost 15% (1.5h), outperforming the insulin-only control. Therefore, glucagon contributes to the safety of an Artificial Pancreas.

Model Reference Adaptive Control of Glucose in Type 1 Diabetics: A Simulation Study

Abstract Paper presents the model reference adaptive control applied for the glucose concentration control in Type 1 diabetes mellitus (T1DM) subject. The adaptive controller structure allows to present the commanded insulin infusion by means of the basal infusion rate and the bolus insulin doses. T1DM simulation model is adjusted so that the simulated output corresponds to the particular data logged in a diabetic diary. These facts have allowed to compare the obtained results with the data logged in the diary.

T1DM subject as a time-delay system: Modeling and adaptive control

Type 1 Diabetes Mellitus (T1DM) subject model is presented in this paper. The model consists of two parts. A glucose and insulin plasma kinetics is inferred from a minimal model of glucose kinetics commonly used to analyze the results of an intravenous glucose tolerance test (IVGTT). A subcutaneous insulin and a subcutaneous glucose kinetics are modeled as a time-delay. A meal announcement information is considered as a measured disturbance signal and a disturbance model is also proposed. The presented model serves as a base for the design of an adaptive control algorithm for automatic normoglycemia maintaining in T1DM subject. The adaptive control algorithm is also briefly presented in the paper.

Bihormonal control of blood glucose in people with type 1 diabetes

This paper presents a bihormonal artificial pancreas (AP) for people with type 1 diabetes (T1D) designed to provide a safe blood glucose control with minimal use of glucagon. The control algorithm uses insulin as well as glucagon to prevent hyper- and hypoglycemia. We employ a novel prediction-based activation of glucagon administration. The control algorithm consists of a Kalman filter, an insulin infusion model predictive controller (MPC), a proportional-derivative (PD) controller for glucagon infusion, and a meal time insulin bolus calculator. The PD controller is activated if the Kalman filter predicts hypoglycemia. Predictions utilize an ARMAX model describing glucose-insulin and glucose-glucagon dynamics. The model parameters are estimated from basic patient-specific data. A continuous glucose monitor provides feedback. We test the control algorithm using a simulation model with time-varying parameters available for 3 patients. We consider a simulation scenario where meals are estimated correctly as well as overestimated by 30%. The simulation results demonstrate that during normal operation, the controller only needs insulin and does not need glucagon. During unexpected events, such as insulin overdose due to an overestimated meal, the control algorithm uses glucagon efficiently to avoid severe hypoglycemia.

Additional Adaptive Controller for Mutual Torque Ripple Minimization in PMSM Drive Systems

Abstract This paper presents an additional adaptive current controller for mutual torque ripple minimization in PMSM. It is assumed that the basis of the control system is a conventional current control loop commonly used in vector control of the PMSM. No change in the structure of current loop is required, only access to the torque reference input is needed. Parameters of the additional controller are adapted directly by the derived adaptation law. Finally, the proposed control scheme is verified by means of simulations.

Individualized T1DM simulator for verification of adaptive controller

Individualized type 1 diabetes mellitus (T1DM) subject model is presented in this paper. Insulin-glucose subsystem based on Bergmans minimal model is coupled with subcutaneous insulin absorption and absorption of digested carbohydrates. Identification of model parameters was performed on pharmacokinetics and pharmacodynamics characteristics of administered insulin and data collected from continuous glucose monitoring (CGM) system. The identified model served as a basis for designing a model reference adaptive controller.

T1DM model parameters adjustment based on PK, PD and CGM data

The aim of this paper is to adjust the parameters of selected type 1 diabetes mellitus (T1DM) simulator so that the output of the simulator corresponds to the continuous glucose monitoring (CGM) data of particular person with diabetes. For an identification of corresponding subsystems of the model, pharmacokinetics (PK) and pharmacodynamics (PD) of the corresponding particular insulin are also used.