Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Diabetes is a chronic disease in which there are high levels of sugar in the blood. Insulin is a hormone that regulates the blood glucose level in the body. Diabetes mellitus can be caused by too little insulin, a resistance to insulin, or both. Although research activities on controlling blood glucose have been attempted to lower the blood glucose level in the quickest possible time, there are some shortages in the amount of the insulin injection. In this paper, a complete model of the glucose--insulin regulation system, which is a nonlinear delay differential model, is used. The purpose of this paper is to follow the glucose profiles of a healthy person with minimum infused insulin. To achieve these purposes, an intelligent fuzzy controller based on a Mamdani-type structure, namely the swarm optimization tuned Mamdani fuzzy controller, is proposed for type 1 diabetic patients. The proposed fuzzy controller is optimized by a novel heuristic algorithm, namely linearly decreasing weight particle swarm optimization. To verify the robust performance of the proposed controller, a group of 4 tests is applied. Insensitivity to multiple meal disturbances, high accuracy, and superior robustness to model the parameter uncertainties are the key aspects of the proposed method. The simulation results illustrate the superiority of the proposed controller.

Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Diabetes is a chronic disease in which there are high levels of sugar in the blood. Insulin is a hormone that regulates the blood glucose level in the body. Diabetes mellitus can be caused by too little insulin, a resistance to insulin, or both. Although research activities on controlling blood glucose have been attempted to lower the blood glucose level in the quickest possible time, there are some shortages in the amount of the insulin injection. In this paper, a complete model of the glucose--insulin regulation system, which is a nonlinear delay differential model, is used. The purpose of this paper is to follow the glucose profiles of a healthy person with minimum infused insulin. To achieve these purposes, an intelligent fuzzy controller based on a Mamdani-type structure, namely the swarm optimization tuned Mamdani fuzzy controller, is proposed for type 1 diabetic patients. The proposed fuzzy controller is optimized by a novel heuristic algorithm, namely linearly decreasing weight particle swarm optimization. To verify the robust performance of the proposed controller, a group of 4 tests is applied. Insensitivity to multiple meal disturbances, high accuracy, and superior robustness to model the parameter uncertainties are the key aspects of the proposed method. The simulation results illustrate the superiority of the proposed controller.

___

  • C. Cobelli, C. Dalla Man, G. Sparacino, L. Magni, G. De Nicolao, B.P. Kovatchev, “Diabetes: models, signals, and control”, IEEE Reviews in Biomedical Engineering, Vol. 2, pp. 54–96, 2009.
  • D.M. Nathan, P.A. Cleary, J.Y. Backlund, S.M. Genuth, J.M. Lachin, T.J. Orchard, P. Raskin, B. Zinman, “Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes”, New England Journal of Medicine, Vol. 353, pp. 2643–2653, 2005.
  • No authors listed, “The effect of intensive diabetes therapy on the development and progression of neuropathy. The Diabetes Control Complications Trial Research Group”, Annals of Internal Medicine, Vol. 122, pp. 561–568, 1995. T.G. Cormack, B. Grant, M.J. Macdonald, J. Steel, I.W. Campbell, “Incidence of blindness due to diabetic eye disease in Fife 1990-9”, British Journal of Ophthalmology, Vol. 85, pp. 354–356, 2001.
  • M.C. Rhatigan, G.P. Leese, J. Ellis, A. Ellingford, A.D. Morris, R.W. Newton, S.T. Roxburgh, “Blindness in patients with diabetes who have been screened for eye disease”, Eye (London), Vol. 13, pp. 166–169, 1999.
  • C. Trautner, A. Icks, B. Haastert, F. Plum, M. Berger, “Incidence of blindness in relation to diabetes. A populationbased study”, Diabetes Care, Vol. 20, pp. 1147–1153, 1997.
  • F.M. Alsaleh, F.J. Smith, S. Keady, K.M. Taylor, “Insulin pumps: From inception to the present and toward the future”, Journal of Clinical Pharmacy and Therapeutics, Vol. 35, pp. 127–138, 2010.
  • I. Conget Donlo, D. Serrano Contreras, J.M. Rodr´ıguez Barrios, I. Levy Mizrahi, C. Castell Abat, S. Roze, “[Costutility analysis of insulin pumps compared to multiple daily doses of insulin in patients with type 1 diabetes mellitus in Spain]”, Revista Espa˜ nola de Salud P´ ublica, Vol. 80, pp. 679–695, 2006 (article in Spanish).
  • J. Kesavadev, A. Kumar, S. Ahammed, S. Jothydev, “Experiences with insulin pump in 52 patients with type 2 diabetes in India”, [abstract 2021-PO] 68th Scientific Sessions of American Diabetes Association, pp. 78–85, 2008. J. Kesavadev, S. Balakrishnan, S. Ahammed, S. Jothydev, “Reduction of glycosylated hemoglobin following 6 months of continuous subcutaneous insulin infusion in an Indian population with type 2 diabetes”, Diabetes Technology & Therapeutics, Vol. 11, pp. 517– 521, 2009.
  • L. Kovfics, B. Pal?ncz, “Glucose insulin control of type 1 diabetic patients in H 2 /H ∞ space via computer algebra”, Proceedings of the 2nd International Conference on Algebraic Biology, pp. 95–109, 2007.
  • L. Magni, D.M. Raimondo, C.D. Man, G. De Nicolao, B. Kovatchev, C. Cobelli, “Model predictive control of glucose concentration in type 1 diabetic patients: an in silico trial”, Biomedical Signal Processing and Control, Vol. 4, pp. 338–346, 2009.
  • G. Marchetti, M. Barolo, L. Jovanovicy, H. Zisser, D.E. Seborg, “An improved PID switching control strategy for type 1 diabetes”, IEEE Transactions on Biomedical Engineering, Vol. 55, pp. 857–865, 2008.
  • E. Renard, G. Costalat, H. Chevassus, J. Bringer, “Closed loop insulin delivery using implanted insulin pumps and sensors in type 1 diabetic patients”, Diabetes Research and Clinical Practice, Vol. 74, S173–S177, 2006.
  • E. Ruiz-Velazqueza, R. Femat, D.U. Campos-Delgadoc, “Blood glucose control for type 1 diabetes mellitus: a robust tracking H ∞ problem”, Control Engineering Practice, Vol. 12, pp. 1179–1195, 2004.
  • H.T. Nguyen, N.R. Prasad, C.L. Walker, E.A. Walker, A First Course in Fuzzy and Neural Control, USA, Chapman & Hall/CRC, 2003.
  • J. Chen, K. Cao, Y. Sun, Y. Xiao, X. Su, “Continuous drug infusion for diabetes therapy: a closed-loop control system design”, European Association for Signal Processing Journal on Wireless Communications and Networking, Vol. 2008, Article No. 44, 2008.
  • C. Li, R. Hu, “Fuzzy-PID control for the regulation of blood glucose in diabetes”, Proceedings of the 2009 WRI Global Congress on Intelligent Systems, pp. 170–174, 2009.
  • M. Ibbini, “A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients”, Journal of Medical Engineering & Technology, Vol. 30, pp. 83–92, 2006.
  • M. Ibbini, M. Masadeh, “A fuzzy logic based closed-loop control system for blood glucose level regulation in diabetics”, Journal of Medical Engineering & Technology, Vol. 29, pp. 64–69, 2005.
  • H. Wang, J. Li, Y. Kuang, “Mathematical modeling and qualitative analysis of insulin therapies”, Mathematical Biosciences, Vol. 210, pp. 17–33, 2007.
  • F.V. Bergh, A.P. Engelbrecht, “A study of particle swarm optimization particle trajectories”, Information Sciences, Vol. 176, pp. 937–971, 2006.
  • J. Kennedy, R.C. Eberhart, The Particle Swarm: Social Adaptation in Informal-Processing Systems: New Ideas in Optimization, McGraw-Hill, 1999.
  • R.C. Eberhart, Y.H. Shi, “Particle swarm optimization: developments, applications and resources”, Proceedings of the Congress on Evolutionary Computation, pp. 81–86, 2001.
  • R. Yang, M. Zhang, T.J. Tarn, “Dynamic modeling and control of a micro-needle integrated piezoelectric micropump for diabetes care”, 6th IEEE Conference on Nanotechnology, Vol. 1, pp. 146–149, 2006.
  • H. Modares, A. Alfi, M.B. Naghibi Sistani, “Parameter estimation of bilinear systems based on an adaptive particle swarm optimization”, Engineering Applications of Artificial Intelligence, Vol. 23, pp. 1105–1111, 2010.
  • A. Ratnaweera, S.K. Halgamuge, H.C. Watson, “Self-organizing hierarchical particle swarm optimizer with timevarying acceleration coefficients”, IEEE Transactions on Evolutionary Computation, Vol. 8, pp. 240–255, 2004.
  • H.L. Kwang, First Course on Fuzzy Theory and Applications, Heidelberg, Springer-Verlag, 2005.
  • M. Al-Fandi, M.A.K. Jaradat, Y. Sardahi, “Optimal PI-fuzzy logic controller of glucose concentration using genetic algorithm”, International Journal of Knowledge-Based and Intelligent Engineering Systems, Vol. 15, pp. 99–117, 20
Turkish Journal of Electrical Engineering and Computer Science-Cover
  • ISSN: 1300-0632
  • Yayın Aralığı: Yılda 6 Sayı
  • Yayıncı: TÜBİTAK