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.
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.
Authors: Jessica L Ruiz; Jennifer L Sherr; Eda Cengiz; Lori Carria; Anirban Roy; Gayane Voskanyan; William V Tamborlane; Stuart A Weinzimer Journal: J Diabetes Sci Technol Date: 2012-09-01
Authors: Boris Kovatchev; Claudio Cobelli; Eric Renard; Stacey Anderson; Marc Breton; Stephen Patek; William Clarke; Daniela Bruttomesso; Alberto Maran; Silvana Costa; Angelo Avogaro; Chiara Dalla Man; Andrea Facchinetti; Lalo Magni; Giuseppe De Nicolao; Jerome Place; Anne Farret Journal: J Diabetes Sci Technol Date: 2010-11-01
Authors: Firas H El-Khatib; Steven J Russell; David M Nathan; Robert G Sutherlin; Edward R Damiano Journal: Sci Transl Med Date: 2010-04-14 Impact factor: 17.956
Authors: Marc Breton; Anne Farret; Daniela Bruttomesso; Stacey Anderson; Lalo Magni; Stephen Patek; Chiara Dalla Man; Jerome Place; Susan Demartini; Simone Del Favero; Chiara Toffanin; Colleen Hughes-Karvetski; Eyal Dassau; Howard Zisser; Francis J Doyle; Giuseppe De Nicolao; Angelo Avogaro; Claudio Cobelli; Eric Renard; Boris Kovatchev Journal: Diabetes Date: 2012-06-11 Impact factor: 9.461
Authors: Stuart A Weinzimer; Jennifer L Sherr; Eda Cengiz; Grace Kim; Jessica L Ruiz; Lori Carria; Gayane Voskanyan; Anirban Roy; William V Tamborlane Journal: Diabetes Care Date: 2012-07-18 Impact factor: 19.112
Authors: Eyal Dassau; Howard Zisser; Rebecca A Harvey; Matthew W Percival; Benyamin Grosman; Wendy Bevier; Eran Atlas; Shahar Miller; Revital Nimri; Lois Jovanovic; Francis J Doyle Journal: Diabetes Care Date: 2012-11-27 Impact factor: 19.112
Authors: Peter G Jacobs; Navid Resalat; Joseph El Youssef; Ravi Reddy; Deborah Branigan; Nicholas Preiser; John Condon; Jessica Castle Journal: J Diabetes Sci Technol Date: 2015-10-05
Authors: Gregory P Forlenza; Faye M Cameron; Trang T Ly; David Lam; Daniel P Howsmon; Nihat Baysal; Georgia Kulina; Laurel Messer; Paula Clinton; Camilla Levister; Stephen D Patek; Carol J Levy; R Paul Wadwa; David M Maahs; B Wayne Bequette; Bruce A Buckingham Journal: Diabetes Technol Ther Date: 2018-04-16 Impact factor: 6.118