An Improved PID Switching Control Strategy for Type 1 Diabetes

Abstract

In order for an "artificial pancreas" to become a reality for ambulatory use, a practical closed-loop control strategy must be developed and critically evaluated. In this paper, an improved PID control strategy for blood glucose control is proposed and evaluated in silico using a physiologic model of Hovorka et al. [1]. The key features of the proposed control strategy are: (i) a switching strategy for initiating PID control after a meal and insulin bolus; (ii) a novel time-varying setpoint trajectory, (iii) noise and derivative filters to reduce sensitivity to sensor noise, and (iv) a systematic controller tuning strategy. Simulation results demonstrate that the proposed control strategy compares favorably to alternatives for realistic conditions that include meal challenges, incorrect carbohydrate meal estimates, changes in insulin sensitivity, and measurement noise.

Diabetes: Models, Signals, and Control

“…Among its pros, there is the possibility of relating the PID tuning parameters to the biometric parameters of the patient [259]–[261]. …”

“…As already mentioned, in order to improve performance, a possibility is to add a feedforward action, so as to recover the promptness of meal compensation. Although MPC strategies appear better suited to incorporate predictions on the future effects of meals, an attempt has been made along this direction by [261], where the authors present a controller switching between PID regulation and bolus administration in proximity of meals. In any case, irrespective of the adopted tuning procedure or the presence of a feedforward action, PID controller will be subject to saturations and therefore an anti-windup implementation is always needed.…”

“…characterizing the physiology of each individual. This can be done for both PID regulators [261] and MPC ones [143], [255]–[257]. In particular, for MPC the relation between biometric and physiological patient’s parameters and the optimal scalar aggressiveness parameter of the controller can be determined through a sequence of in silico trials [255]–[257].…”

An ARO based fuzzy PI+D controller for type 1 diabete

“…Because of the risk caused by great changes of blood glucose and preventing from hypoglycemia and hyperglycemia, the controller are always being optimized and promoted. Among them, PI controllers by Gantt et al (2007), PID by Switching Marchetti (2008) [3]; Sliding mode by Hernandez (2013), MPC method by Sorru et al (2012) can be mentioned [1]. In PI method, a sensor and subcutaneous injection pump were used and it is a function of output error.…”

Effectiveness of Intravenous Infusion Algorithms for Glucose Control in Diabetic Patients Using Different Simulation Models

“…These algorithms suffered from being too patient specific, being sensitive to measurement noise, and in having to be reprogrammed as the patient’s metabolic parameters changed. A recently proposed PID switching algorithm by Marchetti et al [6] was demonstrated to perform favorably when faced with meal disturbances, changes in insulin sensitivity, and intra-patient variability in simulations. Among advanced algorithms, Fisher [7] and Ollerton [8] developed optimal control algorithms, but the authors demonstrated the inability of their algorithms alone to successfully control glucose through simulations.…”