Insulin drug regulation by general type 2 fuzzy controller with alpha plane
Subject Areas : Renewable energyShima Nasr 1 , Hamid Mahmoodian 2
1 - Electrical Engineering Faculty, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 - ٍElectrical Engineering Faculty, Najafabad Branch, Islamic azad University, Najafabad, Iran
Keywords: Genetic Algorithm, Neural network, fuzzy control, Diabetes, blood glucose control,
Abstract :
Insulin therapy with an insulin pump for diabetic patients has different challenges in the real world. Physiological uncertainties in human bodies, different types of daily activities are the most important challenges in this field. Besides, delay in CHO effects in blood glucose may increase the risk of hypoglycemic and hyperglycemic. In this paper, general type 2 fuzzy controller with alpha-plane has been used to handle the uncertainties and a neural network predictor to estimate the blood glucose in next hour as well. Genetic algorithm is also used to tune some free parameters in the controller. in addition, Fuzzy rules have been weighted by predefined values based on the prediction of the amount of glucose in one hour late. in such case, rule weighting has been adjusted according to the glucose of the body which in turn two high risk situations of diabetic patients (hyperglycemia and hypoglycemia) have been considered in fuzzy inference. the Simulation results on Hovorka model shows that the controller can regulate the blood glucose in the existence of uncertainty in model and CHO regimen without the risk of hypoglycemic and hyperglycemic situations.
[1] L. Katrin, T..Singh, M. Walter, M.D. Brendel, S. Leonhardt, “Blood glucose control algorithms for type-1 diabetic patients: A methodological review”, Biomedical Signal Processing And Control, Vol. 8, No. 2, pp.107-119, March 2013 (doi:10.1016/j.bspc.2012.09.003).
[2] G. Xiaoteng, N. Huangjiang, Y. Wang, “Systematically in silico comparison of unihormonal andbihormonal artificial pancreas systems”, Computational and Mathematical Methods in Medicine, Vol. 2013, pp.1-10, 2013 (doi: 10.1155/2013/712496. Epub 2013 Oct 24.).
[3] F. Cameron, D.M. Wilson, B.A. Buckingham, H. Arzumanyan, P. Clinton,H.P. Chase, J. Lum, D.M. Maahs, P.M. Calhoun, B.W. Bequette, “In-patient studies of a Kalman filter based predictive pump shut-off algorithm, J Diabetes Sci Technol, Vol.6, pp.1142–7, Sep. 2012 (doi:10.1177/193229681200600519).
[4] G. Marchetti, M. Barolo, L. Jovanovic, H. Zisser, D.E. Seborg, “An improved PID switching strategy for type 1 diabetes”, IEEE Trans. on Biomed Engineering, Vol. 55, No. 3, pp. 857–65, March 2008 (doi: 10.1109/TBME.2008.915665).
[5] Y. Wang, E. Dassau, F.J. Doyle, “Closed-loop control of artificial pancreatic b-cell in type 1 diabetes mellitus using model predictive iterative learning control, IEEE Trans. on Biomed Engineering, Vol. 57, No. 2, pp.211–9, 2010 (doi: 10.1109/TBME.2009.2024409).
[6] D. Boiroux, A.K.D. Henriksen, S. Schmidt, K. Nørgaard, N.K. Poulsen, H. Madsen, J.B. Jørgensen, “Adaptive control in an artificial pancreas for people with type 1 diabetes”, Control Engineering Practice, Vol. 58, pp. 332-342, Jan. 2017 (doi:10.1016/j.conengprac.2016.01.003).
[7] G. Quiroz, C.P. Flores-Gutierrez, R. Femat, “Suboptimal H-infinity hyperglycemia control on T1DM accounting biosignals of exercise and nocturnal hypoglycemia”, Optimal Control Appllications and Methods, Vol. 32, pp. 239–52, Jan. 2011 (doi: 10.1002/oca.989).
[8] P. Colmegna, R.S.S. Pena, R. Gondhalekar, E. Dassau, F.J. Doyle, “Reducing risks in type 1 diabetes using H∞ control”, IEEE Trans. on Biomedical Engineering, Vol. 61, No. 12, Dec. 2014 (doi:10.1109/TBME.2014.2336772).
[9] D.U. Campos-Delgado, M. Hernandez-Ordonez, R. Femat, A. Gordillo-Moscoso, “Fuzzy-based controller for glucose regulationintype-1diabeticpatientsbysubcutaneousroute”, IEEE Trans. on Biomed Engineering, Vol. 53, No. 11, pp. 2201–10, Nov. 2006 (doi: 10.1109/TBME.2006.879461).
[10] S. Kalateh, S. Ozgoli, M.T. Hamidi-Beheshti, “Venous Thromboembolism Modeling by Fuzzy Method”, Journal of Intelligent Procedures in Electrical Technology, Vol. 5, No. 18, pp. 37-44, Summer 2014.
[11] B.W. Bequette, “Challenges and recent progress in the development of a closed loop artificial pancreas”, Annu Rev Control, Vol. 36, No. 2, pp.255–66, 2012 (doi: 10.1016/j.arcontrol.2012.09.007).
[12] L. Kovács, B. Benyó, J. Bokor, Z. Benyó, “Induced L2-norm minimization of glucose–insulin system for type I diabetic patients”, Computer Methods and Programs in Biomedicine, Vol. 102, No. 2, pp. 105–118, 2011 (doi:10.1016/j.cmpb.2010.06.019).
[13] P. Colmegna, R.S. Sánchez Pena, "Analysis of three T1DM simulation models for evaluating robust closed-loop controllers", Computer Methods and Programs in Biomedicine, Vol. 113, No. 1, pp. 371-382, Jan. 2014, (doi:10.1016/j.cmpb.2013.09.020(
[14] R. Hovorka, V. Canonico, L.J. Chassin, U. Haueter, M. Massi-Benedetti, M.O. Federici, “Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes”, Physiol Meas, pp. 905–20, 2004.
[15] S.H. Andersen, “Software for in silico testing of an artificial pancreas”, Technical University of Denmark, 2014.
[16] M.E. Wilinska. L.J. Chassin, H.C. Schaller, L. Schaupp, T.R. Pieber, R. Hovorka, “Insulin kinetics in type-1 diabetes: Continuous and bolus delivery of rapid acting insulin”, IEEE Trans. on Biomedicine Engineering, Vol. 52, No. 1, pp. 3-12, Jan. 2005 (doi: 10.1109/TBME.2004.839639).
[17] C.D. Man, R.A. Rizza, C. Cobelli, “Meal simulation model of the glucose-insulin system”, IEEE Trans. on Biomedicine Engineering, Vol. 54, pp.1740-9, 2007 (doi:10.1109/TBME.2007.893506).
[18] L. Bally, H. Thabit, S. Hartnell, E. Andereggen, Y. Ruan, M.E. Wilinska, M.L. Evans, M.M. Wertli, A.P. Coll, C.Stettler, R. Hovorka, “Closed-loop insulin delivery for glycemic control in noncritical care”, The new England Journal of Medicine, Vol. 379, No. 6, pp. 547-556, Aug. 2018 (doi:10.1056/NEJMoa1805233).
[19]E. Semizer, M. Yüceer, I. Atasoy, R. Berber, “Comparison of control algorithms for the blood glucose concentration in avirtualpatientwithanartificialpancreas”, Chemical Engineering Research and Design,Vol. 90, pp. 926-937, 2012.
[20] D. Zhai, J.M. Mendel, “Uncertainty measures for general type-2 fuzzy sets”, Information Sciences, Vol. 181, No. 3, pp. 503-518, Feb. 2011 (doi:10.1016/j.ins.2010.09.020).
[21] F. Liu, “An efficient centroid type-reductionstrategy for general type-2 fuzzy logicsystem”, Information Sciences, Vol. 178, No. 9, pp. 2224-2236, May 2008 (doi:10.1016/j.ins.2007.11.014).
[22] D. Wu. J.M. Mendel, “Enhancedkarnik–mendelalgorithms”, IEEE Trans..on Fuzzy Systems, Vol. 17, No. 4, pp. 923–934, Aug. 2009 (doi:10.1109/TFUZZ.2008.924329).
_||_