Adaptive control of artificial pancreas systems - a review. (original) (raw)
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Adaptive control of artificial pancreas systems for treatment of type 1 diabetes
Elsevier eBooks, 2020
Artificial pancreas (AP) systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes.
Adaptive control in an artificial pancreas for people with type 1 diabetes
Control Engineering Practice, 2017
In this paper, we discuss overnight blood glucose stabilization in patients with type 1 diabetes using a Model Predictive Controller (MPC). We compute the model parameters in the MPC using a simple and systematic method based on a priori available patient information. We describe and compare 3 different model structures. The first model structure is an autoregressive integrated moving average with exogenous input (ARIMAX) structure. The second model structure is an autoregressive moving average with exogenous input (ARMAX) model, i.e. a model without an integrator. The third model structure is an adaptive ARMAX model in which we use a recursive extended least squares (RELS) method to estimate parameters of the stochastic part. In addition, we describe some safety layers in the control algorithm that improve the controller robustness and reduce the risk of hypoglycemia. We test and compare our control strategies using a virtual clinic of 100 randomly generated patients with a representative intersubject variability. This virtual clinic is based on the Hovorka model. We consider the case where only half of the meal bolus is administered at mealtime, and the case where the insulin sensitivity increases during the night. The numerical results suggest that the use of an integrator leads to higher occurrence of hypoglycemia than for the controllers without the integrator. Compared to the other control strategies, the adaptive MPC reduces both the time spent in hypoglycemia and the time spent in hyperglycemia.
Abstract Background: Accurate closed-loop control is essential for developing artificial pancreas (AP) systems that adjust insulin infusion rates from insulin pumps. Glucose concentration information from continuous glucose monitoring (CGM) systems is the most important information for the control system. Additional physiological measurements can provide valuable information that can enhance the accuracy of the control system. Proportional-integral-derivative control and model predictive control have been popular in AP development. Their implementations to date rely on meal announcements (e.g., bolus insulin dose based on insulin:carbohydrate ratios) by the user. Adaptive control techniques provide a powerful alternative that do not necessitate any meal or activity announcements. Materials and Methods: Adaptive control systems based on the generalized predictive control framework are developed by extending the recursive modeling techniques. Physiological signals such as energy expenditure and galvanic skin response are used along with glucose measurements to generate a multiple-input-single-output model for predicting future glucose concentrations used by the controller. Insulin-on-board (IOB) is also estimated and used in control decisions. The controllers were tested with clinical studies that include seven cases with three different patients with type 1 diabetes for 32 or 60 h without any meal or activity announcements. Results: The adaptive control system kept glucose concentration in the normal preprandial and postprandial range (70-180 mg/dL) without any meal or activity announcements during the test period. After IOB estimation was added to the control system, mild hypoglycemic episodes were observed only in one of the four experiments. This was reflected in a plasma glucose value of 56 mg/dL (YSI 2300 STAT; Yellow Springs Instrument, Yellow Springs, OH) and a CGM value of 63 mg/dL). Conclusions: Regulation of blood glucose concentration with an AP using adaptive control techniques was successful in clinical studies, even without any meal and physical activity announcement.
Artificial Pancreas Systems: An Integrated Multivariable Adaptive Approach
An artificial pancreas (AP) system with a hypoglycemia early alarm system and adaptive control system based on multivariable recursive time series models is developed. The inputs of the model include glucose concentration (GC) and physiological signals that provide information about the physical activities and stress of the patient. The stability of the recursive time-series models is guaranteed by a constrained optimization method. Generalized predictive control (GPC) is used to regulate GC. Experiments in a clinical setting illustrate the performance of the AP and compare it to open-loop regulation by the patient. Results show that the AP can regulate GC successfully and prevent hypoglycemia in spite of exercise.
Robust controller for artificial pancreas for patients with type-1 diabetes
Research on Biomedical Engineering
The target of this paper is to design a simple and an efficient controller for artificial pancreas (AP) system for blood glucose (BG) regulation in type-1 diabetic mellitus (T1DM) patient. Bergman's intravenous model is chosen for the controller design as the model is minimum ordered model of T1DM patient. Method A multi-objective output feedback controller has been designed for AP system considering robustness, disturbance rejection, and transient criterion. Considering H ∞ , pole-placement, and H 2 constraints, a control algorithm has been developed and has been solved using linear matrix inequality (LMI) technique. Result As a testing platform of the intravenous model-based designed controller, UVa/Padova T1DM metabolic simulator has been chosen which uses subcutaneous insulin delivery method. The controller has been tested in the presence of unannounced meal disturbances. Experimental results show that the controller regulates BG level very efficiently with lesser amount of insulin and avoids hypoglycemia effect. In the presence of external noises like glucose sensor noise and insulin pump error, the robustness of the controller has been checked. The performance of the controller has been compared with compound internal model control (IMC) strategy reported earlier for same meal scenario. Conclusion The designed multi-objective controller gives better performance metrices than compound IMC controller.
An Integrated Multivariable Artificial Pancreas Control System
The objective was to develop a closed-loop (CL) artificial pancreas (AP) control system that uses continuous measurements of glucose concentration and physiological variables, integrated with a hypoglycemia early alarm module to regulate glucose concentration and prevent hypoglycemia. Eleven open-loop (OL) and 9 CL experiments were performed. A multivariable adaptive artificial pancreas (MAAP) system was used for the first 6 CL experiments. An integrated multivariable adaptive artificial pancreas (IMAAP) system consisting of MAAP augmented with a hypoglycemia early alarm system was used during the last 3 CL experiments. Glucose values and physical activity information were measured and transferred to the controller every 10 minutes and insulin suggestions were entered to the pump manually. All experiments were designed to be close to real-life conditions. Severe hypoglycemic episodes were seen several times during the OL experiments. With the MAAP system, the occurrence of severe hypoglycemia was decreased significantly (P < .01). No hypoglycemia was seen with the IMAAP system. There was also a significant difference (P < .01) between OL and CL experiments with regard to percentage of glucose concentration (54% vs 58%) that remained within target range (70-180 mg/dl). Integration of an adaptive control and hypoglycemia early alarm system was able to keep glucose concentration values in target range in patients with type 1 diabetes. Postprandial hypoglycemia and exercise-induced hypoglycemia did not occur when this system was used. Physical activity information improved estimation of the blood glucose concentration and effectiveness of the control system.
Biomedical Signal Processing and Control, 2019
Artificial pancreas enables closed loop automated control for blood glucose regulation in type 1 diabetic patients. Simple backstepping controller for regulation of blood glucose level has recently been proposed in the literature. In this research work, we have proposed backstepping based adaptive controller for integration in artificial pancreas. Controller design has been based on Bergman's minimal model which represents the dynamics of blood glucose-insulin system of human body. Glucose effectiveness factor has been treated as an unknown parameter and its value has been adapted using Lyapunov based adaptive backstepping control approach. Effects of meal disturbance, physical non-linearities and sensor noise have also been considered in the controller design. A complete mathematical derivation of the proposed nonlinear controller has been described and simulation results have been presented using Matlab/Simulink environment. Results indicate improvement in tracking response and overshoot/undershoot characteristics as compared to some recently developed techniques in literature. Proposed controller assures dynamic stability against disturbances and deviations in human body parameters. Practical implementation of the proposed controller can result in better artificial pancreas.
Multivariable Adaptive Identification and Control for Artificial Pancreas Systems
A constrained weighted recursive least squares method is proposed to provide recursive models with guaranteed stability and better performance than models based on regular identification methods in predicting the variations of blood glucose concentration in patients with Type 1 Diabetes. Use of physiological information from a sports armband improves glucose concentration prediction and enables earlier recognition of the effects of physical activity on glucose concentration. Generalized predictive controllers (GPC) based on these recursive models are developed. The performance of GPC for artificial pancreas systems is illustrated by simulations with UVa-Padova simulator and clinical studies. The controllers developed are good candidates for artificial pancreas systems with no announcements from patients.
Artificial pancreas: model predictive control design from clinical experience
Journal of diabetes science and technology, 2013
The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator. A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The pr...
The Next Generation of Artificial Pancreas Control Algorithms
2008
Abbreviations: (AP) artificial pancreas, (GIP) gastric inhibitory peptide, (GIT) gastrointestinal tract, (GLP-1) glucagon-like peptide 1, (HPA) hypothalamic-pituitary-adrenal axis, (MPC) model-predictive controller, (PB-PK-PD) physiologically based pharmacokineticspharmacodynamics, (PID) proportional-integral-derivative controller