Sliding-mode disturbance observers for an artificial pancreas without meal announcement
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Beatriz Ricarte | Jorge Bondia | Iván Sala-Mira | J. Bondia | J. Díez | Beatriz Ricarte | José-Luis Díez | I. Sala-Mira
[1] Darrell M. Wilson,et al. A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator , 2009, Journal of diabetes science and technology.
[2] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..
[3] Qian Wang,et al. A Variable State Dimension Approach to Meal Detection and Meal Size Estimation: In Silico Evaluation Through Basal-Bolus Insulin Therapy for Type 1 Diabetes , 2017, IEEE Transactions on Biomedical Engineering.
[4] Xinghuo Yu,et al. Euler's Discretization of Single Input Sliding-Mode Control Systems , 2007, IEEE Transactions on Automatic Control.
[5] Niels Kjølstad Poulsen,et al. Fault and meal detection by redundant continuous glucose monitors and the unscented Kalman filter , 2017, Biomed. Signal Process. Control..
[6] Leonid M. Fridman,et al. Second-order sliding-mode observer for mechanical systems , 2005, IEEE Transactions on Automatic Control.
[7] Raghunathan Rengaswamy,et al. A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..
[8] Garry M. Steil,et al. Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes , 2009, Journal of diabetes science and technology.
[9] R. Rabasa-Lhoret,et al. The challenges of achieving postprandial glucose control using closed‐loop systems in patients with type 1 diabetes , 2018, Diabetes, obesity & metabolism.
[10] F. Doyle,et al. Detection of a Meal Using Continuous Glucose Monitoring , 2008, Diabetes Care.
[11] G. Rossi. Diagnosis and Classification of Diabetes Mellitus , 2011, Diabetes Care.
[12] R. Hovorka,et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.
[13] Jaime A. Moreno,et al. A Lyapunov approach to second-order sliding mode controllers and observers , 2008, 2008 47th IEEE Conference on Decision and Control.
[14] José Luis Díez,et al. Insulin limitation in the Artificial Pancreas by Sliding Mode Reference Conditioning and Insulin Feedback: an in silico comparison , 2017 .
[15] Vincent Acary,et al. Implicit Euler numerical simulations of sliding mode systems , 2009 .
[16] R. Bergman,et al. Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.
[17] Roman Hovorka,et al. Simulation Environment to Evaluate Closed-Loop Insulin Delivery Systems in Type 1 Diabetes , 2010, Journal of diabetes science and technology.
[18] Anirban Roy,et al. The effect of insulin feedback on closed loop glucose control. , 2011, The Journal of clinical endocrinology and metabolism.
[19] Dale E. Seborg,et al. Control-Relevant Models for Glucose Control Using A Priori Patient Characteristics , 2012, IEEE Transactions on Biomedical Engineering.
[20] C. Cobelli,et al. The UVA/PADOVA Type 1 Diabetes Simulator , 2014, Journal of diabetes science and technology.
[21] Vincent Acary,et al. Nonsmooth Modeling and Simulation for Switched Circuits , 2010, Lecture Notes in Electrical Engineering.
[22] Josep Vehí,et al. Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring , 2018, Sensors.
[23] R. Hovorka. Continuous glucose monitoring and closed‐loop systems , 2006, Diabetic medicine : a journal of the British Diabetic Association.
[24] Cesar C. Palerm,et al. Physiologic insulin delivery with insulin feedback: A control systems perspective , 2011, Comput. Methods Programs Biomed..
[25] Christofer Toumazou,et al. A Simple Robust Method for Estimating the Glucose Rate of Appearance from Mixed Meals , 2012, Journal of diabetes science and technology.
[26] M. B. Zarrop,et al. Sliding Mode Observers for Robust Sensor Monitoring , 1996 .
[27] Beatriz Ricarte,et al. Insulin Estimation and Prediction: A Review of the Estimation and Prediction of Subcutaneous Insulin Pharmacokinetics in Closed-Loop Glucose Control , 2018, IEEE Control Systems.
[28] H. De Battista,et al. Safety Auxiliary Feedback Element for the Artificial Pancreas in Type 1 Diabetes , 2013, IEEE Transactions on Biomedical Engineering.
[29] T. Kawamura,et al. The factors affecting on estimation of carbohydrate content of meals in carbohydrate counting , 2015, Clinical pediatric endocrinology : case reports and clinical investigations : official journal of the Japanese Society for Pediatric Endocrinology.
[30] A. Levant. Robust exact differentiation via sliding mode technique , 1998 .
[31] R. Ferry,et al. Real-Time Support of Pediatric Diabetes Self-Care by a Transport Team , 2013, Diabetes Care.
[32] K. Turksoy,et al. Real-Time Insulin Bolusing for Unannounced Meals Using CGM Measurements , 2015 .
[33] Kirsten Nørgaard,et al. Bolus Calculators , 2014, Journal of diabetes science and technology.
[34] Ahmad Haidar,et al. The Artificial Pancreas and Meal Control: An Overview of Postprandial Glucose Regulation in Type 1 Diabetes , 2018, IEEE Control Systems.
[35] Yu Liu,et al. Coupled disturbance reconstruction by sliding mode observer approach for nonlinear system , 2017 .
[36] F. Doyle,et al. Design of the Glucose Rate Increase Detector , 2014, Journal of diabetes science and technology.
[37] Jorge Bondia,et al. Generalized extended state observer design for the estimation of the rate of glucose appearance in artificial pancreas , 2018, 2018 European Control Conference (ECC).
[38] Christopher Edwards,et al. Sliding Mode Control and Observation , 2013 .