CONTROL OF BLOOD GLUCOSE IN TYPE 1 DIABETIC PATIENTS

By

ONIFADE, DAMILOLA VICTORIA(Matriculation Number, 092318)

Department of Chemical Engineering,Ladoke Akintola University of Technology,

Ogbomoso, Oyo State.

Under the supervision ofDr. S.E. Agarry and

Dr. D.O. Araromi

March, 2015

Introduction Problem Statement Aims and Objectives Literature Review Methodology Results and Discussion Conclusion References

OUTLINES

Globally, as of 2013, an estimated 382 million people have diabetes worldwide, and it is responsible for 1.5 to 5.1 million deaths per year making it the 8th leading cause of death worldwide. (International Diabetes Federation, 2013) Considering these death records, control of blood glucose and insulin injection are important to the affected patients, health personnel and even the government. It is therefore necessary to devise a closed loop system which combine glucose monitoring and insulin delivery technology by means of an algorithm (Klonoff, 2007) A closed-loop abiotic artificial pancreas system to control blood glucose levels is a potential cure for diabetes. (Clarke and Foster, 2012).

PROBLEM STATEMENT Considering the risk of serious clinical complications and the cost that comes with intravenous injection of insulin for treatment of diabetic patients, there is a need to devise a more accurate way of controlling diabetes. Although there have been different semi-closed loop system for the treatment of diabetes, it has become necessary to develop a closed loop system in order to eliminate the complications of manual intervention.

INTRODUCTION

The aim of this project is to develop a closed-loop system for maintaining the normal blood glucose concentration in the body system of a Type 1 diabetic patient.

This aim was achieved through the following objectives:1. Simulation of type 1 diabetic patient using the Bergman’s

Minimal Model and linearization of the model.2. Control of the model using Internal Model Control (IMC)

procedure3. Stability test of the closed-loop system using bode stability

criterion.

AIM AND OBJECTIVES

Literature reviewName of Author/Year

Work done Remark

Parker et al., He worked use of a model predictive controller (MPC), for regulating blood glucose. Controller synthesis was accomplished by linearizing a modified version of Bergman’s nonlinear patient model. Constraints on insulin delivery rate and rate of change were included in the control algorithm, and the linear controller was evaluated in simulation studies using the nonlinear model as the patient.

Disturbance rejection and hyperglycemic initial condition simulations showed the efficacy of the controller, which maintained glucose above the hypoglycemic bound

Ibbini, (2008) The superiority of using fuzzy logic control strategies for the case of regulating the normoglycemic average for type-I diabetic patients was demonstrated. He also showed and demonstrated the utility of fine tuning of the fuzzy logic control parameters and found be of value to obtain better results with respect to response characteristics and smoothness.

Through tuning of the FLC and PI-FLC parameters (Ke, Kr, KΔu) and found be of value to obtain better results with respect to response characteristics and smoothness.

Author/Year Work-done Remark

Sriram et. al., (2010) Design of a model based control (IMC) for blood glucose in diabetic patient alongside testing of the performance of designed controller for servo-regulatory cases; the robustness analysis and the performance of entire blood glucose system by varying single tunable parameter(lamda) was also analysed

It was observed that an increase in lamda caused a more sluggish response. The controller was able to maintain set point from all the responses gotten regardless of the tuning parameter. However, it could not regulate disturbance at all tuning parameter value.

Literature Review(contd)

METHODOLOGY

Process Stability and Linearization

Simulink Modeling

Internal Model Control

Model Order Reduction of Controller

SISO Design of PID, IMC and LQG Controllers

Stability Test (Bode Stability Criterion)

S/N Symbol Variable Name Value

1 GbBasal Plasma Glucose 4.5mmol/liter

2 IbBasal Plasma Insulin 4.5mU/liter

3 V1Insulin Distribution Volume 12liter

4 p1Insulin Dependent constant 0 min-1

5 p2Delay in Insulin Action 0.025 min-1

6 p3the insulin-dependent increase in glucose uptake

ability

0.0000013 (mU.min/L) -1

7 N Fractional dissapearance rate of insulin 5/54 min-1

8 G Blood Glucose Concentration 4.5mmol/liter

9 I Insulin Concentration 10.5mU/liter

10 X Insulin Concentration (Remote Compartment) the

insulin’s effect on the net glucose disappearance

0.005461 min-1

Table 1: Table of Operating Data :culled from Bequette, (2002) and Sriram et. al. (2010).

RESULTS AND DISCUSSION

The state space representation and process transfer function obtained from linearization are given below.

The controller transfer function obtained was given as:

Figure 4: Controller model order reduction step response

Figure 3: System Response to Setpoint Changes

Figure 2: System Response to Changes in Disturbance

Figure 1: System Response to Tuning

Internal Model Controller Analysis Plotst

Figure 5: Step Response of LQG Controller

Figure 6: Step Response of IMC

Figure 7: Step Response of PID Controller Figure 8: Closed Loop System Response

SISO Design Analysis Plots

Figure 9: System Response of Manual and Automatically Tuned IMC Controller

Figure 10: Bode Stability Plot for Manually Tuned Controller

Figure 11: Bode Stability Plot for automatically tuned Controller

System Response and Bode Plots

Conclusion

The simulation results of the diabetic patient should be implemented practically on real processes probably an infected animal, subjected to scrutiny based on the real time observation and then upgraded and optimized for desired performance.

Considering the results presented, it can be concluded that: Linearization of a process model is more accurately done using the control and estimation tool manger in MATLAB. Internal model control strategy is more suitable to develop a closed loop system for blood glucose regulation. The internal model controller developed is stable.

Recommendatio

n

Bequette, B. W. (2002). Process Control: Modeling, Design, and Simulation. New Jersey: Prentice Hall PTR.

Clarke, S. F., & Foster, J. (2012). A history of Blood Glucose Meters and their Role in Self Monitoring of Diabetes Mellitus. British Journal of Biomedical Science , 83-93.

Doyle, F., Jovanovic, L., & Seborg, D. (2007). Glucose control strategies for treating type 1 diabetes. Journal of Process Control , 572-576.

Ibbini, M. S. (2008). Comparative Study of Different Control Techniques. International Conference on BIOMEDICAL ELECTRONICS and BIOMEDICAL INFORMATICS (pp. 106-110). Rhodes: Ramtha-Irbid.

International Diabetes Federation(IDF). (2013). Diabetes Mellitus. Retrieved from IDF website : www.idf.org

Klonoff, C. D. (2007). The Artificial Pancreas: How Sweet Engineering will solve Bitter Problems. Journal of Diabetes Science and Technology , 72-81.

Parker, S. R., Doyle, J. F., & Peppas, A. N. (2001). A Review of Control Algorithms for Noninvasive Monitoring. IEEE ENGINEERING IN MEDICINE AND BIOLOGY , 65-73.

Sriram, R., Srinivasan, K., & T, V. R. (2010). Internal Model Control Design for Blood Glucose In Diabetic Patients. International Journal of Pharmaceutical and Applied Sciences , 48-51.

REFERENCES

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