Abstract: Techniques are used for adaptation of drug-administration parameters that control insulin delivery in a blood glucose control system. One technique provides long-term adaptation of a nominal basal infusion rate, adapting to longer-term changes in a patient's needs due to growth, illness, hormonal fluctuations, physical activity, aging, etc. Another technique provides adaptation of priming dose size at mealtimes for overall better glycemic control and also adapting to longer-term changes in a patient's needs. Adaptation calculations use a receding-horizon window of recent values of the adapted parameter. Doses of a counter-regulatory agent (e.g., glucagon) may also be delivered in response to information about estimated accumulation of exogenously infused insulin (subcutaneously, intramuscularly, intraperitoneally, or intravenously) and/or the effect insulin might have on glucose levels (blood glucose concentration or interstitial fluid glucose concentration).

Abstract: Techniques are used for adaptation of drug-administration parameters that control insulin delivery in a blood glucose control system. One technique provides long-term adaptation of a nominal basal infusion rate, adapting to longer-term changes in a patient's needs due to growth, illness, hormonal fluctuations, physical activity, aging, etc. Another technique provides adaptation of priming dose size at mealtimes for overall better glycemic control and also adapting to longer-term changes in a patient's needs. Adaptation calculations use a receding-horizon window of recent values of the adapted parameter. Doses of a counter-regulatory agent (e.g., glucagon) may also be delivered in response to information about estimated accumulation of exogenously infused insulin (subcutaneously, intramuscularly, intraperitoneally, or intravenously) and/or the effect insulin might have on glucose levels (blood glucose concentration or interstitial fluid glucose concentration).

Abstract: Techniques are used for adaptation of drug-administration parameters that control insulin delivery in a blood glucose control system. One technique provides long-term adaptation of a nominal basal infusion rate, adapting to longer-term changes in a patient's needs due to growth, illness, hormonal fluctuations, physical activity, aging, etc. Another technique provides adaptation of priming dose size at mealtimes for overall better glycemic control and also adapting to longer-term changes in a patient's needs. Adaptation calculations use a receding-horizon window of recent values of the adapted parameter. Doses of a counter-regulatory agent (e.g., glucagon) may also be delivered in response to information about estimated accumulation of exogenously infused insulin (subcutaneously, intramuscularly, intraperitoneally, or intravenously) and/or the effect insulin might have on glucose levels (blood glucose concentration or interstitial fluid glucose concentration).

Abstract: Techniques are used for adaptation of drug-administration parameters that control insulin delivery in a blood glucose control system. One technique provides long-term adaptation of a nominal basal infusion rate, adapting to longer-term changes in a patient's needs due to growth, illness, hormonal fluctuations, physical activity, aging, etc. Another technique provides adaptation of priming dose size at mealtimes for overall better glycemic control and also adapting to longer-term changes in a patient's needs. Adaptation calculations use a receding-horizon window of recent values of the adapted parameter. Doses of a counter-regulatory agent (e.g., glucagon) may also be delivered in response to information about estimated accumulation of exogenously infused insulin (subcutaneously, intramuscularly, intraperitoneally, or intravenously) and/or the effect insulin might have on glucose levels (blood glucose concentration or interstitial fluid glucose concentration).

Abstract: An augmented, adaptive algorithm utilizing model predictive control (MPC) is developed for closed-loop glucose control in type 1 diabetes. A linear empirical input-output subject model is used with an MPC algorithm to regulate blood glucose online, where the subject model is recursively adapted, and the control signal for delivery of insulin and a counter-regulatory agent such as glucagon is based solely on online glucose concentration measurements. The MPC signal is synthesized by optimizing an augmented objective function that minimizes local insulin accumulation in the subcutaneous depot and control signal aggressiveness, while simultaneously regulating glucose concentration to a preset reference set point. The mathematical formulation governing the subcutaneous accumulation of administered insulin is derived based on nominal temporal values pertaining to the pharmacokinetics (time-course of activity) of insulin in human, in terms of its absorption rate, peak absorption time, and overall time of action.

Abstract: An augmented, adaptive algorithm utilizing model predictive control (MPC) is developed for closed-loop glucose control in type 1 diabetes. A linear empirical input-output subject model is used with an MPC algorithm to regulate blood glucose online, where the subject model is recursively adapted, and the control signal for delivery of insulin and a counter-regulatory agent such as glucagon is based solely on online glucose concentration measurements. The MPC signal is synthesized by optimizing an augmented objective function that minimizes local insulin accumulation in the subcutaneous depot and control signal aggressiveness, while simultaneously regulating glucose concentration to a preset reference set point. The mathematical formulation governing the subcutaneous accumulation of administered insulin is derived based on nominal temporal values pertaining to the pharmacokinetics (time-course of activity) of insulin in human, in terms of its absorption rate, peak absorption time, and overall time of action.

Abstract: An augmented, adaptive algorithm utilizing model predictive control (MPC) is developed for closed-loop glucose control in type 1 diabetes. A linear empirical input-output subject model is used with an MPC algorithm to regulate blood glucose online, where the subject model is recursively adapted, and the control signal for delivery of insulin and a counter-regulatory agent such as glucagon is based solely on online glucose concentration measurements. The MPC signal is synthesized by optimizing an augmented objective function that minimizes local insulin accumulation in the subcutaneous depot and control signal aggressiveness, while simultaneously regulating glucose concentration to a preset reference set point. The mathematical formulation governing the subcutaneous accumulation of administered insulin is derived based on nominal temporal values pertaining to the pharmacokinetics (timecourse of activity) of insulin in human, in terms of its absorption rate, peak absorption time, and overall time of action.

Abstract: An augmented, adaptive algorithm utilizing model predictive control (MPC) is developed for closed-loop glucose control in type 1 diabetes. A linear empirical input-output subject model is used with an MPC algorithm to regulate blood glucose online, where the subject model is recursively adapted, and the control signal for delivery of insulin and a counter-regulatory agent such as glucagon is based solely on online glucose concentration measurements. The MPC signal is synthesized by optimizing an augmented objective function that minimizes local insulin accumulation in the subcutaneous depot and control signal aggressiveness, while simultaneously regulating glucose concentration to a preset reference set point. The mathematical formulation governing the subcutaneous accumulation of administered insulin is derived based on nominal temporal values pertaining to the pharmacokinetics (timecourse of activity) of insulin in human, in terms of its absorption rate, peak absorption time, and overall time of action.