An adaptive backstepping based non-linear controller for artificial pancreas in type 1 diabetes patients (original) (raw)
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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.
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 of artificial pancreas systems - a review.
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.
Integral Backstepping based Automated Control of Blood Glucose in Diabetes Mellitus Type 1 Patients
IEEE Access
Diabetes Mellitus Type 1 happens when our immune system destroys beta cells in our pancreas due to which it fails to produce enough insulin; a hormone which allows sugar/glucose to enter in its cells in order to produce energy. To cope with failure of pancreas, artificial ones are used to inject the required amount of insulin in the body. Controllers are used for automatic balancing of blood glucose-insulin level. Bergman's minimal model (BMM) is a physiologically verified model representing this phenomenon. In a recent research BMM is extended to more generic form with an extended state of the system, dealing with the disturbance to the blood glucose level caused by meal intake during medication. In this research paper, we have used BMM along with its extended model and proposed three nonlinear controllers: Integral Backstepping (IBS) Controller, Backstepping (BS) Controller and Fuzzy Logic Controller (FLC), for the automatic stabilization of the blood glucose level in Diabetes Mellitus Type 1 patients. The integral action is integrated with Backstepping technique; resulting in reducing steady-state error by significant amount. A mathematical analysis has been done to prove the asymptotic stability of the proposed controllers for the both models using Lyapunov theory. For showing the tracking behavior of the proposed controllers with their respective models to the desired blood-glucose output, simulation results have been performed and discussed using MATLAB/Simulink. Comparison results of the both systems show that the proposed controllers performs far better than the ones given in the literature.
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.
Design of backstepping LQG controller for blood glucose regulation in type I diabetes patient
International Journal of Automation and Control, 2020
The mechanisation of the insulin infusion regulation through the artificial pancreas (AP) is needed to design with an objective to control the blood glucose (BG) level in type 1 diabetes mellitus (T1DM) patients. However, to make it applicable in real time, major components needed are availability of proper sensor augmented pumps, glucose monitoring systems, and control techniques. Recent times many researchers suggest robust control techniques for designing a robust controller for computing the required insulin dose for a highly nonlinear human metabolism system. This paper proposes a simulation model of glucose metabolism process and design of a backstepping linear quadratic Gaussian controller (BLQGC) to control the BG level in TIDM patients. The simulations are carried out through MATLAB/SIMULINK environment and the results indicate comparatively better control ability of the proposed algorithm to control the BG concentration within the range of normoglycaemia in terms of accuracy, stability, quick damping and robustness.
Backstepping Nonlinear Control for Blood Glucose Based on Sliding Mode Meal Observer
Al-Nahrain Journal for Engineering Sciences
Diabetes is one of the most critical diseases in the world which requires measuring the concentration of glucose also the injection of insulin to control the glucose rate in the body. The proposed controller is applied to the Bergman’s three-state minimal patient model, where the model is considered certain but with unknown meal. In the present work, a nonlinear controller is designed to control the concentration of glucose based on the Backstepping approached with a sliding mode for observing the disturbance meal. So will have estimated the meal and have canceled the effect that the glucose concentration has regulating to the basal level. The effectiveness of the proposed controller, which represent the insulin dose, is proved via simulating the Bergman’s model with designed controller via MATLAB Simulink software. The result clarify the ability and the robustness of the proposed controller.
Self-tuning controller for regulation of glucose levels in patients with type 1 diabetes
2008
Abstract Closing the loop with a fully automated artificial pancreas will definitely improve the life of patients with type 1 diabetes. An adaptive control strategy is proposed in order to dynamically respond to unpredicted glucose fluctuations due to internal or external perturbations. The adaptability of the controller is further assured with models derived from continuous glucose measuring device data collected from the patient.