Observer-Based State Feedback for Enhanced Insulin Control of Type ‘I’ Diabetic Patients

Abstract

During the past few decades, biomedical modeling techniques have been applied to improve performance of a wide variety of medical systems that require monitoring and control. Diabetes is one of the most important medical problems. This paper focuses on designing a state feedback controller with observer to improve the performance of the insulin control for type ‘I’ diabetic patients. The dynamic model of glucose levels in diabetic patients is a nonlinear model. The system is a typical fourth-order single-input-single-output state space model. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance. But it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified control scheme using observer-based feedback controllers. By comparing different control schemes, it shows that a properly designed state feedback controller with observer can eliminate the adaptation strategy that the Proportional-Integral-Derivative (PID) controllers need to improve the control performance. Control strategies are simulated and their performance is evaluated in MATLAB and Simulink.

A Novel Artificial Pancreas: Energy Efficient Valveless Piezoelectric Actuated Closed-Loop Insulin Pump for T1DM

“…Ackerman [18,31,32], developed a model utilizing four first-order rate constants for the glucose-insulin interactions. The model is described on two poles; the first one is G = blood glucose level; another one is H = hormone level.…”

Observer Design for a Nonlinear Minimal Model of Glucose Disappearance and Insulin Kinetics

“…In this paper the considered nonlinear minimal GD-IK model is a combination of models extracted from papers of (Hariri and Wang, 2011), (Percival et al, 2008):…”

“…The parameters and initial states used in the simulations are: P 1 = 0 P 2 = 0.81/100, P 3 = 4.01/1e6 n = 0.23, a = 2, gb = 99, ib = 8 , γ = 2.4/1000, h = 93 , x 1 (0) = 337 , x 2 (0) = 0, x 3 (0) = 192 , x 4 (0) = 2. These parameters and initial states are the same as in (Hariri and Wang, 2011).…”

“…Most research which is so far interested in control or observer synthesis using the minimal GD-IK model is based on linearization of this model, see (Percival et al, 2008), (Magni et al, 2007), (Hariri and Wang, 2011), (González and Femat, 2011), (Parker et al, 1999), (Bergman et al, 1979), (L Kovcs, 2007) and references therein. More recent work on the same theme can be found in (Eberle and Ament, 2012) and (Villafaa-Rojas et al, 2013).…”

Adaptive fractional-order blood glucose regulator based on high-order sliding mode observer

“…Hence, in some researches, an observer is designed to estimate these states of BMM that are inaccessible. In [21 ] BMM is linearised and then an observer‐based state feedback is designed to enhance insulin control of Type I diabetic patient, and then the Linear Quadratic Regulator method optimised that observer [22 ]. Most researches interested in controller or observer synthesis used the linearised BMM [23 ].…”