Diseño robusto de un observador de perturbaciones con saturaciones: Aplicación al control de regulación de la glucosa en pacientes con diabetes tipo 1
DOI:
https://doi.org/10.4995/riai.2023.19773Palabras clave:
diabetes, control robusto, elipsoide atractivo, observador de estados extendido generalizado, optimizaciónResumen
La diabetes mellitus tipo 1 requiere de un estricto control en la administración de insulina para evitar consecuencias graves derivadas de la hiperglucemia y la hipoglucemia. El concepto de páncreas artificial permite la automatización en el tratamiento de pacientes con esta enfermedad, sin embargo, requiere de algoritmos de control capaces de operar eficientemente para mantener la concentración de glucosa en la sangre en niveles apropiados. Estos niveles apropiados en conjunto con el hecho de que el controlador no puede eliminar insulina del sistema nos indican que la salida y la entrada se encuentran acotadas, lo cual es considerado en el diseño del controlador para mejorar su desempeño. Debido a la presencia de incertidumbres y perturbaciones externas se propone el uso de un controlador robusto basado en un observador de estados extendidos generalizado (EGESO) que asegure una operación eficiente que evite episodios de hiperglucemia e hipoglucemia. Con el uso del EGESO se pueden estimar tanto los estados del sistema como las perturbaciones, lo cual elimina la necesidad de conocer información sobre las horas de ingesta, así como las cantidades a ingerir. La estabilidad del sistema de control propuesto es asegurada mediante el método del elipsoide atractivo y la solución de un problema de optimización basado en desigualdades matriciales bilineales (BMI). El desempeño del esquema de control propuesto es verificado mediante pruebas de simulación en Simulink, donde se observa que el controlador propuesto emula la terapia de bucleabierto en la cual el paciente debe administrar un bolo de insulina de forma paralela a cada ingesta.
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Alazki, H., Poznyak, A., June 2013. A class of robust bounded controllers tracking a nonlinear discrete-time stochastic system: Attractive ellipsoid technique application. Journal of the Franklin Institute 350 (5), 1008 - 1029. https://doi.org/10.1016/j.jfranklin.2013.02.001
Bally, L., Thabit, H., Kojzar, H., Mader, J. K., Qerimi-Hyseni, J., Hartnell, S., Tauschmann, M., Allen, J. M., Wilinska, M. E., Pieber, T. R., et al., 2017. Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. The Lancet Diabetes & Endocrinology 5 (4), 261-270. https://doi.org/10.1016/S2213-8587(17)30001-3
Batiha, I. M., Oudetallah, J., Ouannas, A., Al-Nana, A. A., Jebril, I. H., 2021. Tuning the fractional-order pid-controller for blood glucose level of diabetic patients. Int. J. Advance Soft Compu. Appl 13 (2), 1-10.
Castillo, A., Garcia, P., Sanz, R., Albertos, P., 2018. Enhanced extended state observer-based control for systems with mismatched uncertainties and disturbances. ISA transactions 73. https://doi.org/10.1016/j.isatra.2017.12.005
Castillo, A., Garcia Gil, P. J., Albertos, P., 2022. Reguladores basados en observadores de perturbaciones: principios de funcionamiento y m'etodos de diseno. Revista Iberoamericana de Autom'atica e Inform'atica industrial 19 (4), 343-355. https://doi.org/10.4995/riai.2022.16856
Chen, W.-H., Yang, J., Guo, L., Li, S., 2015. Disturbance-observer-based control and related methods - an overview. IEEE Transactions on industrial electronics 63 (2), 1083-1095. https://doi.org/10.1109/TIE.2015.2478397
Cinar, A., Turksoy, K., 2018. Advances in Artificial Pancreas Systems: Adaptive and Multivariable Predictive Control. Springer. https://doi.org/10.1007/978-3-319-72245-0
Control, D., Group, C. T. R., 1993. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulindependent diabetes mellitus. New England journal of medicine 329 (14), 977-986. https://doi.org/10.1056/NEJM199309303291401
Cortes-Vega, D., Alazki, H., 2021. Robust Maximum Power Point Tracking scheme for pv systems based on attractive ellipsoid method. Sustainable Energy, Grids and Networks 25, 100410. https://doi.org/10.1016/j.segan.2020.100410
De Pereda, D., Romero-Vivo, S., Ricarte, B., Rossetti, P., Ampudia-Blasco, F. J., Bondia, J., 2016. Real-time estimation of plasma insulin concentration from continuous glucose monitor measurements. Computer methods in biomechanics and biomedical engineering 19 (9), 934-942. https://doi.org/10.1080/10255842.2015.1077234
Dubey, V., et al., 2021. Comparative analysis of pid tuning techniques for blood glucose level of diabetic patient. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12 (11), 2948-2953. https://doi.org/10.17762/turcomat.v12i2.1433
Eberle, C., Ament, C., 2012. Real-time state estimation and long-term model adaptation: a two-sided approach toward personalized diagnosis of glucose and insulin levels. Journal of diabetes science and technology 6 (5), 1148- 1158. https://doi.org/10.1177/193229681200600520
Franco, R., de Loza, A. F., Rios, H., Cassany, L., Gucik-Derigny, D., Cieslak, J., Henry, D., Olccomendy, L., 2021. Output-feedback sliding-mode controller for blood glucose regulation in critically ill patients affected by type 1 diabetes. IEEE Transactions on Control Systems Technology 29 (6), 2704- 2711. https://doi.org/10.1109/TCST.2020.3046420
Gondhalekar, R., Dassau, E., Doyle III, F. J., 2016. Periodic zone-mpc with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes. Automatica 71, 237-246. https://doi.org/10.1016/j.automatica.2016.04.015
Gondhalekar, R., Dassau, E., Doyle III, F. J., 2018. Velocity-weighting & velocity-penalty mpc of an artificial pancreas: Improved safety & performance. Automatica 91, 105-117. https://doi.org/10.1016/j.automatica.2018.01.025
Goodwin, G. C., Carrasco, D. S., Seron, M. M., Medioli, A. M., 2018. A fundamental control performance limit for a class of positive nonlinear systems. Automatica 95, 14-22. https://doi.org/10.1016/j.automatica.2018.05.008
Goodwin, G. C., Medioli, A. M., Carrasco, D. S., King, B. R., Fu, Y., 2015. A fundamental control limitation for linear positive systems with application to type 1 diabetes treatment. Automatica 55, 73-77. https://doi.org/10.1016/j.automatica.2015.02.041
Gregory, G. A., Robinson, T. I., Linklater, S. E., Wang, F., Colagiuri, S., de Beaufort, C., Donaghue, K. C., Magliano, D. J., Maniam, J., Orchard, T. J., et al., 2022. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. The Lancet Diabetes & Endocrinology 10 (10), 741-760. https://doi.org/10.1016/S2213-8587(22)00218-2
Heydarinejad, H., Delavari, H., Baleanu, D., 2019. Fuzzy type-2 fractional backstepping blood glucose control based on sliding mode observer. International journal of dynamics and control 7, 341-354. https://doi.org/10.1007/s40435-018-0445-8
Hovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., Pieber, T. R., Schaller, H. C., Schaupp, L., Vering, T., et al., 2004. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological measurement 25 (4), 905. https://doi.org/10.1088/0967-3334/25/4/010
Kanderian, S. S., Weinzimer, S., Voskanyan, G., Steil, G. M., 2009. Identification of intraday metabolic profiles during closed-loop glucose control in individuals with type 1 diabetes. Journal of Diabetes Science and Technology. https://doi.org/10.1177/193229680900300508
Khan, M.W., Abid, M., Khan, A. Q., Mustafa, G., Ali, M., Khan, A., 2020. Sliding mode control for a fractional-order non-linear glucose-insulin system. IET Systems Biology 14 (5), 223-229. https://doi.org/10.1049/iet-syb.2020.0030
Kovatchev, B. P., Gonder-Frederick, L. A., Cox, D. J., Clarke,W. L., 2004. Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose-error grid analysis illustrated by therasense freestyle navigator data. Diabetes Care 27 (8), 1922-1928. https://doi.org/10.2337/diacare.27.8.1922
Li, S., Yang, J., Chen,W.-H., Chen, X., 2011. Generalized extended state observer based control for systems with mismatched uncertainties. IEEE Transactions on Industrial Electronics 59 (12), 4792-4802. https://doi.org/10.1109/TIE.2011.2182011
Ly, T. T., Roy, A., Grosman, B., Shin, J., Campbell, A., Monirabbasi, S., Liang, B., von Eyben, R., Shanmugham, S., Clinton, P., et al., 2015. Day and night closed-loop control using the integrated medtronic hybrid closed-loop system in type 1 diabetes at diabetes camp. Diabetes Care 38 (7), 1205-1211. https://doi.org/10.2337/dc14-3073
Magni, L., Raimondo, D. M., Bossi, L., Dalla Man, C., De Nicolao, G., Kovatchev, B., Cobelli, C., 2007. Model predictive control of type 1 diabetes: an in silico trial. Journal of Diabetes Science and Technology 1 (6). https://doi.org/10.1177/193229680700100603
Medtronic, 2023. Sistema integrado Minimed 780G.https://www.medtronic-diabetes.com/es-ES/sistema-integrado-minimed-780g.
Ordaz, P., Alazki, H., Poznyak, A., 2013. A sample-time adjusted feedback for robust bounded output stabilization. Kybernetika 49 (6), 911 - 934.
Ordaz, P., Poznyak, A., Dec. 2016. Adaptive-robust stabilization of the furuta's pendulum via attractive ellipsoid method. Journal of Dynamic Systems, Measurement and Control 138. https://doi.org/10.1115/1.4032130
Poznyak, A., Polyakov, A., Azhmyakov, V., 2014. Attractive Ellipsoids in Robust Control. Birkhauser Basel. https://doi.org/10.1007/978-3-319-09210-2
Sala, I., Diez, J.-L., Bondia, J., 2018. Generalized extended state observer design for the estimation of the rate of glucose appearance in artificial pancreas. In: 2018 European Control Conference (ECC). IEEE, pp. 2393-2398. https://doi.org/10.23919/ECC.2018.8550123
Sanz, R., Garcia, P., Diez, J.-L., Bondia, J., 2020. Artificial pancreas system with unannounced meals based on a disturbance observer and feedforward compensation. IEEE Transactions on Control Systems Technology 29 (1), 454-460. https://doi.org/10.1109/TCST.2020.2975147
Sanz, R., Garc'ıa, P., Romero-Viv'o, S., D'ıez, J., Bondia, J., 2023. Near-optimal feedback control for postprandial glucose regulation in type 1 diabetes. ISA transactions 133, 345-352. https://doi.org/10.1016/j.isatra.2022.06.033
Turksoy, K., Hajizadeh, I., Samadi, S., Feng, J., Sevil, M., Park, M., Quinn, L., Littlejohn, E., Cinar, A., 2017. Real-time insulin bolusing for unannounced meals with artificial pancreas. Control Engineering Practice 59, 159-164. https://doi.org/10.1016/j.conengprac.2016.08.001
Walsh, J., Roberts, R., 2006. Pumping insulin: everything you need for success on a smart insulin pump. Vol. 4. Torrey Pines Press San Diego, CA.
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Derechos de autor 2023 Hussain Alazki, David Cortés-Vega, Pedro García
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
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