An Improved PID Switching Control Strategy for Type 1 Diabetes

Marchetti, Gianni; Barolo, Massimiliano; Jovanovič, Lois; Zisser, Howard; Seborg, Dale E.

doi:10.1109/iembs.2006.259541

Cited by 56 publications

(83 citation statements)

References 9 publications

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“…A PID controller has been used to attempt to manage glucose with an artifical pancreas by [10,11,12,13,14,15], an approach which seems to be supported by this work.…”

Section: Parameter Estimationmentioning

confidence: 74%

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A new general glucose homeostatic model using a proportional-integral-derivative controller

Watson

Chappell

Ducrozet

et al. 2011

Computer Methods and Programs in Biomedicine

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The glucose-insulin system is a challenging process to model due to the feedback mechanisms present, hence the implementation of a model-based approach to the system is an on-going and challenging research area. A new approach is proposed here which provides an effective way of characterising glycaemic regulation. The resulting model is built on the premise that there are three phases of insulin secretion, similar to those seen in a proportional-integral-derivative (PID) type controller used in engineering control problems. The model relates these three phases to a biological understanding of the system, as well as the logical premise that the homeostatic mechanisms will maintain very tight control of the system. It includes states for insulin, glucose, insulin action and a state to simulate an integral function of glucose.Structural identifiability analysis was performed on the model to determine whether a unique set of parameter values could be identidied from the available observations, which should permit meaningful conclusions to be drawn from parameter estimation. Although two parameters -glucose production rate and the proportional control coefficient -were found to be unidentifiable, the former is not a concern as this is known to be impossible to measure without a tracer ex- * Principle corresponding author * * Corresponding author

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“…A PID controller has been used to attempt to manage glucose with an artifical pancreas by [10,11,12,13,14,15], an approach which seems to be supported by this work.…”

Section: Parameter Estimationmentioning

confidence: 74%

“…those diabetics who do not secrete any insulin. There have been attempts to produce an artifical pancreas using continous glucose monitors with a PID controller to regulate the infusion of insulin [10,11,12,13,14,15].…”

Section: Model Conceptmentioning

confidence: 99%

A new general glucose homeostatic model using a proportional-integral-derivative controller

Watson

Chappell

Ducrozet

et al. 2011

Computer Methods and Programs in Biomedicine

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show abstract

“…Measurement noise and delay, and sensor dynamics can also be accommodated, as demonstrated in related studies [28,29].…”

Section: Introductionmentioning

confidence: 98%

“…In a recent study [28,29] the authors proposed and evaluated a novel PID plus meal bolus control strategy for type 1 diabetes. The key features are: i.…”

Section: A Pid Switching Control Strategymentioning

confidence: 99%

A feedforward–feedback glucose control strategy for type 1 diabetes mellitus

Marchetti

Barolo

Jovanovič

et al. 2008

Journal of Process Control

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As the "artificial pancreas" becomes closer to reality, automated insulin delivery based on real-time glucose measurements becomes feasible for people with diabetes. This paper is concerned with the development of novel feedforward-feedback control strategies for real-time glucose control and type 1 diabetes. Improved post-meal responses can be achieved by a pre-prandial snack or bolus, or by reducing the glucose setpoint prior to the meal. Several feedforward-feedback control strategies provide attractive alternatives to the standard meal insulin bolus and are evaluated in simulations using a physiological model.

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“…There have been multiple algorithms proposed for closed-loop control of glycemia, including MPC [7]- [10], PD [11]- [13], PID [14]- [17], and H ∞ [18], [19] -for a recent review of the literature in this area see [20]. All of these controllers are based on the assumption that a continuous glucose sensor is available, and although sensor technology has improved significantly over the last few years, it is still short of the reliability and accuracy required for commercial implementation of such a closed-loop system [20].…”

Section: Introductionmentioning

confidence: 99%

A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes

Palerm

Zisser

Jovanovič

et al. 2008

Journal of Process Control

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104

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Maintaining good glycemic control is a daily challenge for people with type 1 diabetes. Insulin requirements are changing constantly due to many factors, such as levels of stress and physical activity. The basal insulin requirement also has a circadian rhythm, adding another level of complexity. Automating the adjustment of insulin dosing would result in improved glycemic control, as well as an improved quality of life by significantly reducing the burden on the patient. Building on our previous success of using run-to-run control for prandial insulin dosing (a strategy adapted from the chemical process industry), we show how this same framework can be used to adjust basal infusion profiles. We present a mathematical model of insulin-glucose dynamics which we augment in order to capture the circadian variation in insulin requirements. Using this model, we show that the run-to-run framework can also be successfully applied to adjust basal insulin dosing.

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An Improved PID Switching Control Strategy for Type 1 Diabetes

Cited by 56 publications

References 9 publications

A new general glucose homeostatic model using a proportional-integral-derivative controller

A new general glucose homeostatic model using a proportional-integral-derivative controller

A feedforward–feedback glucose control strategy for type 1 diabetes mellitus

A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes

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