Journal of Computational Applied Mechanics

Fractional Order PID Controller for Diabetes Patients

Document Type : Research Paper

Authors

1 Department of Mechanical Engineering, Ferdowsi University of Mashhad

2 Department of Life Science Engineering, University of Tehran

3 Department of life science engineering, University of Tehran

Abstract

This paper proposes an optimized control policy over type one diabetes. Type one diabetes is taken into consideration as a nonlinear model (Augmented Minimal Model), which is implemented in MATLAB-SIMULINK. This Model is developed in consideration of the patient's conditions. There are some uncertainties in the regarded model due to factors such as blood glucose concentration, daily meals or sudden stresses. Moreover, there are distinct approaches toward the elimination of these uncertainties. In here, a meal is fed to the model as an input in order to omit these uncertainties. Also, different control methods could be chosen to monitor the blood glucose level. In this paper, a Fractional Order PID is utilized as the control method. Thereafter, the control method and parameters are tuned by conducting genetic algorithm, as a powerful evolutionary algorithm. Finally, the output of the optimized Fractional order PID and traditional PID control method, which had the same parameters as the Fractional PID except the fractions, are compared. At the end, it is concluded by utilizing Fractional Order PID, not only the controller performance improved considerably, but also, unlike the traditional PID, the blood glucose concentration is maintained in the desired range.

Keywords

[1]. Centers-for-Disease-Control-Prevention, National diabetes fact sheet: national estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, 2011. 201.
[2]. Wild, S., et al., Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes care, 2004. 27(5): pp. 1047-1053.
[3]. American-Diabetes-Association, Standards of medical care in diabetes—2013. Diabetes care, 2013. 36(Suppl 1): p. S11.
[4]. Tchobroutsky, G., Relation of diabetic control to development of microvascular complications. Diabetologia, 1978. 15(3): pp. 143-152.
[5]. Pietri, A., F.L. Dunn, and P. Raskin, The effect of improved diabetic control on plasma lipid and lipoprotein levels: a comparison of conventional therapy and continuous subcutaneous insulin infusion. Diabetes, 1980. 29(12): pp. 1001-1005.
[6]. Cobelli, C., et al., Diabetes: models, signals, and control. Biomedical Engineering, IEEE Reviews in, 2009. 2: pp. 54-96.
[7]. M. Goharimanesh, A. Lashkaripour, S. Shariatnia, A. Akbari, Diabetic Control Using Genetic Fuzzy-PI Controller. International Journal of Fuzzy Systems, 2014. 16(2): pp. 133-139.
[8]. Ǻström, K.J. and T. Hägglund, PID controllers: theory, design, and tuning. Instrument Society of America, Research Triangle Park, NC, 1995.
[9]. Cominos, P. and N. Munro, PID controllers: recent tuning methods and design to specification. IEE Proceedings-Control Theory and Applications, 2002. 149(1): pp. 46-53.
[10]. Åström, K.J. and T. Hägglund, Advanced PID control. 2006: ISA-The Instrumentation, Systems, and Automation Society; Research Triangle Park, NC 27709.
[11]. Åström, K.J. and T. Hagglund, Automatic tuning of PID controllers. 1988: Instrumentation, Systems, and Automation Society.
[12]. Zhuang, M. and D. Atherton. Automatic tuning of optimum PID controllers. in Control Theory and Applications, IEE Proceedings D. 1993. IET.
[13]. Pan, I., S. Das, and A. Gupta, Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISA transactions, 2011. 50(1): pp. 28-36.
[14]. Padula, F. and A. Visioli, Tuning rules for optimal PID and fractional-order PID controllers. Journal of Process Control, 2011. 21(1): pp. 69-81.
[15]. Cao, J. Y., J. Liang, and B.-G. Cao. Optimization of fractional order PID controllers based on genetic algorithms. in Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. 2005. IEEE.
[16]. Ramprasad, Y., G. Rangaiah, and S. Lakshminarayanan, Robust PID controller for blood glucose regulation in type I diabetics. Industrial & engineering chemistry research, 2004. 43(26): pp. 8257-8268.
[17]. Ibbini, M., A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients. Journal of medical engineering & technology, 2006. 30(2): pp. 83-92.
[18]. Markakis, M.G., G.D. Mitsis, and V.Z. Marmarelis. Computational study of an augmented minimal model for glycaemia control. in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. 2008. IEEE.
[19]. Brunner, G.A., et al., Validation of home blood glucose meters with respect to clinical and analytical approaches. Diabetes Care, 1998. 21(4): pp. 585-590.
[20]. Podlubny, I., Fractional-order systems and fractional-order controllers. The Academy of Sciences Institute of Experimental Physics, UEF03-94, Kosice, Slovak Republic, 1994.
[21]. Podlubny, I., Fractional-order systems and PI/sup/spl lambda//D/sup/spl mu//-controllers. Automatic Control, IEEE Transactions on, 1999. 44(1): pp. 208-214.
[22]. Oustaloup, A., et al., Frequency-band complex noninteger differentiator: characterization and synthesis. Circuits and
Systems I: Fundamental Theory and Applications, IEEE Transactions on, 2000. 47(1): pp. 25-39.
Journal of Computational Applied Mechanics
Volume 46, Issue 1 - Serial Number 1
Winter & Spring
January 2015
Pages 69-76
  • Receive Date: 01 October 2014
  • Revise Date: 04 April 2015
  • Accept Date: 03 April 2015